CISC_873_DM_F22_A1.ipynb

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      "authorship_tag": "ABX9TyNGDnSbsyuR3nfk1sWBQ8Rk",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mostafa3zazi/CISC-873-DM-Data-Mining/blob/main/CISC_873_DM_F22_A1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CISC-873-DM-F22-A1: Wish.com Product Rating Prediction"
      ],
      "metadata": {
        "id": "CS1SgQCbJx3T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Download data from kaggle"
      ],
      "metadata": {
        "id": "OJVdPE2RE8bg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BE_3pLlyC0yP",
        "outputId": "46b807aa-139c-4dbc-e6be-f80a719b8721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install kaggle --upgrade"
      ],
      "metadata": {
        "id": "Y3_sUwWTCsxA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp '/content/drive/MyDrive/Colab Notebooks/kaggle.json' ~/.kaggle/"
      ],
      "metadata": {
        "id": "MQTtGUT4D2b_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb227db4-1b97-4f3f-abf5-59d854020964"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "kY_9YEADDREz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPaeJaLbDb9_",
        "outputId": "a782014f-7a56-4924-a3fb-74bf64e57cc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kaggle API 1.5.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhisc8Z2Ce5l",
        "outputId": "6b9eb08f-dc47-4fd7-d9b6-3d9ae413f718"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "train_new.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
            "sample_submission_new.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
            "test_new.csv: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions download -c cisc-873-dm-f22-a1"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Defining the problem\n",
        "The goal is to predict the product ratings given the other features known for a product on Wish.com. Ratings are in categories from 1 to 5."
      ],
      "metadata": {
        "id": "dUum1rUBI0r-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import libraries for data exploration and processing\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "FcaS7og4EJcQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#read train and test files\n",
        "df_train = pd.read_csv('train_new.csv')\n",
        "df_test = pd.read_csv('test_new.csv')"
      ],
      "metadata": {
        "id": "FFKv-yf5FGLX"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df_train.head()"
      ],
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          "height": 374
        },
        "id": "KxNEUFtdFQsX",
        "outputId": "c6625d08-e53f-4efd-a307-d1f2f60084ac"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>88 % avis positifs (66,644 notes)</td>\n",
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              "      <td>https://s3-us-west-1.amazonaws.com/sweeper-pro...</td>\n",
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              "      <td>上海铭启电气科技有限公司</td>\n",
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              "      <th>4</th>\n",
              "      <td>8.00</td>\n",
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              "      <td>91 % avis positifs (173 notes)</td>\n",
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            "text/plain": [
              "   price  retail_price currency_buyer  ...   theme  crawl_month    id\n",
              "0   8.00             7            EUR  ...  summer      2020-08   272\n",
              "1   8.00             7            EUR  ...  summer      2020-08  1515\n",
              "2   6.00            34            EUR  ...  summer      2020-08   940\n",
              "3   4.67             4            EUR  ...  summer      2020-08   309\n",
              "4   8.00            11            EUR  ...  summer      2020-08   226\n",
              "\n",
              "[5 rows x 34 columns]"
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          "metadata": {},
          "execution_count": 114
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        "id": "GcYmR8PxFVuX",
        "outputId": "4de59eb5-8310-4ff9-c446-292961e75dac"
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      "execution_count": null,
      "outputs": [
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1094 entries, 0 to 1093\n",
            "Data columns (total 34 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   price                         1094 non-null   float64\n",
            " 1   retail_price                  1094 non-null   int64  \n",
            " 2   currency_buyer                1094 non-null   object \n",
            " 3   units_sold                    1094 non-null   int64  \n",
            " 4   uses_ad_boosts                1094 non-null   int64  \n",
            " 5   rating                        1094 non-null   float64\n",
            " 6   rating_count                  1094 non-null   int64  \n",
            " 7   badges_count                  1094 non-null   int64  \n",
            " 8   badge_local_product           1094 non-null   int64  \n",
            " 9   badge_product_quality         1094 non-null   int64  \n",
            " 10  badge_fast_shipping           1094 non-null   int64  \n",
            " 11  tags                          1094 non-null   object \n",
            " 12  product_color                 1065 non-null   object \n",
            " 13  product_variation_size_id     1082 non-null   object \n",
            " 14  product_variation_inventory   1094 non-null   int64  \n",
            " 15  shipping_option_name          1094 non-null   object \n",
            " 16  shipping_option_price         1094 non-null   int64  \n",
            " 17  shipping_is_express           1094 non-null   int64  \n",
            " 18  countries_shipped_to          1094 non-null   int64  \n",
            " 19  inventory_total               1094 non-null   int64  \n",
            " 20  has_urgency_banner            349 non-null    float64\n",
            " 21  urgency_text                  349 non-null    object \n",
            " 22  origin_country                1082 non-null   object \n",
            " 23  merchant_title                1094 non-null   object \n",
            " 24  merchant_name                 1090 non-null   object \n",
            " 25  merchant_info_subtitle        1094 non-null   object \n",
            " 26  merchant_rating_count         1094 non-null   int64  \n",
            " 27  merchant_rating               1094 non-null   float64\n",
            " 28  merchant_id                   1094 non-null   object \n",
            " 29  merchant_has_profile_picture  1094 non-null   int64  \n",
            " 30  merchant_profile_picture      170 non-null    object \n",
            " 31  theme                         1094 non-null   object \n",
            " 32  crawl_month                   1094 non-null   object \n",
            " 33  id                            1094 non-null   int64  \n",
            "dtypes: float64(4), int64(16), object(14)\n",
            "memory usage: 290.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "some columns have so many null values so we will drop them <br>\n",
        "like has_urgency_banner , urgency_text and merchant_profile_picture <br>\n",
        "some columns are not relevat to our taks and like: <br>\n",
        "id, merchant_title, merchant_name, merchant_info_subtitle merchant_has_profile_picture"
      ],
      "metadata": {
        "id": "WapjUnbxWKyb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Inspect each column\n",
        "here we inspect each column by using pandas value_counts function to find the value counts of each unique value. Then comment on the findings in a markdown cell below each column inspection."
      ],
      "metadata": {
        "id": "AqMKfmz9ZXCm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#price and ratial_price\n",
        "df_train['price'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLzYdm-OZa49",
        "outputId": "8af8ba0a-3844-4ae5-bb61-c4009fab9793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.00     196\n",
              "11.00    135\n",
              "7.00      89\n",
              "6.00      88\n",
              "9.00      88\n",
              "        ... \n",
              "25.00      1\n",
              "5.70       1\n",
              "3.90       1\n",
              "4.69       1\n",
              "4.73       1\n",
              "Name: price, Length: 117, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['retail_price'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rfbvpLKMZt2e",
        "outputId": "da3def28-eaee-435c-8d2e-5b1ef74e95fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7      127\n",
              "6      103\n",
              "10      86\n",
              "11      73\n",
              "5       71\n",
              "      ... \n",
              "87       1\n",
              "139      1\n",
              "37       1\n",
              "53       1\n",
              "57       1\n",
              "Name: retail_price, Length: 95, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the ratial price should be heigher than or equla to the price so let's check"
      ],
      "metadata": {
        "id": "ID1ytvdoc3ac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(df_train['price'] > df_train['retail_price']).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7P1scjDrc0Ql",
        "outputId": "e79f282e-138e-4e14-ee61-f3042b2b537b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "391"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I think there are 391 entries that should be droped where price > ratail price"
      ],
      "metadata": {
        "id": "h2PeedUmdgzs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#currency_buyer\n",
        "df_train['currency_buyer'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lr4EBvFZZ0nd",
        "outputId": "fd699f68-cb2f-465a-ae5c-5dfae44aa57a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EUR    1094\n",
              "Name: currency_buyer, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "currency_buyer should be dropped at it contains only one unique value"
      ],
      "metadata": {
        "id": "Q8WUpirleIqc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#units_sold\n",
        "df_train['units_sold'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dQzM7g-eHxs",
        "outputId": "7491de51-7530-4b2d-b930-e86edcca9825"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100       353\n",
              "1000      280\n",
              "5000      139\n",
              "10000     125\n",
              "20000      79\n",
              "50         54\n",
              "10         37\n",
              "50000      12\n",
              "100000      5\n",
              "8           4\n",
              "1           3\n",
              "2           2\n",
              "7           1\n",
              "Name: units_sold, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#uses_ad_boosts\n",
        "df_train['uses_ad_boosts'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GID6FHf-eSj2",
        "outputId": "25fa2c49-1ea1-4563-bea5-670582d4640b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    621\n",
              "1    473\n",
              "Name: uses_ad_boosts, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#raiting and raiting counts\n",
        "df_train['rating'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_v2yCmieSh9",
        "outputId": "9b698525-ecbc-4135-9370-06d1b39716c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    774\n",
              "5.0    170\n",
              "3.0    135\n",
              "2.0     12\n",
              "1.0      2\n",
              "6.0      1\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we have product with raiting 6!! ... must drop these values"
      ],
      "metadata": {
        "id": "1MhymKugfQXE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['rating_count'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zb2Z4QIOfP7z",
        "outputId": "fc81a13d-d68f-483b-eca2-5e9da2dfb492"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       30\n",
              "2       27\n",
              "4       22\n",
              "6       17\n",
              "10      15\n",
              "        ..\n",
              "1183     1\n",
              "370      1\n",
              "569      1\n",
              "4421     1\n",
              "135      1\n",
              "Name: rating_count, Length: 584, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the previous cell shows that there are 30 entries with zero rating count"
      ],
      "metadata": {
        "id": "UA67cLPcfhMM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[df_train['rating_count'] == 0][['rating','rating_count']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "2yjrucGreSf0",
        "outputId": "18060db3-57ff-4d35-fee1-267b8d87f9d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            ],
            "text/plain": [
              "      rating  rating_count\n",
              "66       5.0             0\n",
              "99       5.0             0\n",
              "113      5.0             0\n",
              "149      5.0             0\n",
              "181      5.0             0\n",
              "240      5.0             0\n",
              "243      5.0             0\n",
              "249      5.0             0\n",
              "254      5.0             0\n",
              "276      5.0             0\n",
              "291      5.0             0\n",
              "346      5.0             0\n",
              "390      5.0             0\n",
              "402      5.0             0\n",
              "406      5.0             0\n",
              "409      5.0             0\n",
              "439      5.0             0\n",
              "497      5.0             0\n",
              "527      5.0             0\n",
              "597      5.0             0\n",
              "626      5.0             0\n",
              "764      5.0             0\n",
              "901      5.0             0\n",
              "904      5.0             0\n",
              "911      5.0             0\n",
              "962      5.0             0\n",
              "991      5.0             0\n",
              "994      5.0             0\n",
              "999      5.0             0\n",
              "1032     5.0             0"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "these entries have raiting of 5 so they should be dropped"
      ],
      "metadata": {
        "id": "rGsoxbdlf2V0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# badges_count , badge_local_product , badge_product_quality , badge_fast_shipping\n",
        "df_train['badges_count'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1zkUsGDjhxmi",
        "outputId": "b88e9a4c-cfbe-48bf-cc74-4977f0cb1ffe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    979\n",
              "1    106\n",
              "2      8\n",
              "3      1\n",
              "Name: badges_count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['badge_product_quality'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32IpdBk9iW6T",
        "outputId": "5d44fbe0-7a9f-4352-81a2-200da84d4684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1003\n",
              "1      91\n",
              "Name: badge_product_quality, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['badge_product_quality'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ilu25GXicvz",
        "outputId": "42f53573-8274-4ba7-be58-c42ad21cc865"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1003\n",
              "1      91\n",
              "Name: badge_product_quality, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['badge_fast_shipping'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_80_sQEwim-D",
        "outputId": "6f8d0d38-c9b7-4b12-84c0-b4f272815535"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1081\n",
              "1      13\n",
              "Name: badge_fast_shipping, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will check the correlation between these columns and the rating later on."
      ],
      "metadata": {
        "id": "xQFaEka5zHmv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#product_color\n",
        "df_train['product_color'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGftpZ2Zishe",
        "outputId": "0afd007b-7548-409a-fb9d-b16d9fd4abd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "black             219\n",
              "white             173\n",
              "blue               77\n",
              "yellow             69\n",
              "pink               68\n",
              "                 ... \n",
              "claret              1\n",
              "brown & yellow      1\n",
              "whitestripe         1\n",
              "Pink                1\n",
              "light green         1\n",
              "Name: product_color, Length: 87, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#product_variation_size_id\n",
        "df_train['product_variation_size_id'].value_counts().index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Ggp4oeg55PS",
        "outputId": "cee46350-e1b4-461a-a77b-4ee42debc019"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['S', 'XS', 'M', 'XXS', 'L', 'S.', 'XL', 'XXL', 'XXXS', 'M.', 'Size-XS',\n",
              "       'Size S', '4XL', 'XS.', 'Size-S', '2XL', '10 ml', 's', 'Size M', '35',\n",
              "       'Size4XL', 'Size -XXS', 'SizeL', '25', 'SIZE XS', '29', 'S Pink',\n",
              "       'XXXXXL', '33', '2pcs', '5', 'One Size', '20PCS-10PAIRS', '2',\n",
              "       '4-5 Years', 'Size-5XL', '30 cm', '1 PC - XL', 'size S', 'Size/S', 'B',\n",
              "       '20pcs', 'S..', 'Suit-S', 'Base & Top & Matte Top Coat',\n",
              "       'Baby Float Boat', '1m by 3m', 'SIZE S', 'White', '40 cm', '5XL',\n",
              "       '10pcs', 'Round', '1 pc.', '100 cm', '60', '26(Waist 72cm 28inch)',\n",
              "       'S(bust 88cm)', 'choose a size', 'Base Coat',\n",
              "       '100 x 100cm(39.3 x 39.3inch)', '1', '25-S', 'Size-XXS', '5PAIRS',\n",
              "       'Pack of 1', '6XL', 'S/M(child)', 'S (waist58-62cm)', 'Size S.',\n",
              "       'SIZE XXS', 'X   L', 'Women Size 36', '04-3XL',\n",
              "       'Floating Chair for Kid', '34', 'US-S', 'Size XXS', 'pants-S', 'XXXXL',\n",
              "       'SIZE-XXS', '1pc', 'H01'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['product_variation_size_id'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ks-AumyezV2c",
        "outputId": "60f5ccf9-a70e-41f7-aefa-8f96f8953275"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S           444\n",
              "XS          248\n",
              "M           135\n",
              "XXS          76\n",
              "L            29\n",
              "           ... \n",
              "pants-S       1\n",
              "XXXXL         1\n",
              "SIZE-XXS      1\n",
              "1pc           1\n",
              "H01           1\n",
              "Name: product_variation_size_id, Length: 83, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I don't think product_variation_size_id will have great effect on rating"
      ],
      "metadata": {
        "id": "dyTAkfxVo4bI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#product_variation_inventory\n",
        "df_train['product_variation_inventory'].value_counts()"
      ],
      "metadata": {
        "id": "oLSCtgxDisa6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c79c7bde-5da1-45ab-cf76-f683db87b60f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50    626\n",
              "1     114\n",
              "2      54\n",
              "5      51\n",
              "3      38\n",
              "10     26\n",
              "9      17\n",
              "7      15\n",
              "4      14\n",
              "6      12\n",
              "18      9\n",
              "17      9\n",
              "15      9\n",
              "20      8\n",
              "13      6\n",
              "25      6\n",
              "49      5\n",
              "19      5\n",
              "14      5\n",
              "46      4\n",
              "11      4\n",
              "8       4\n",
              "30      4\n",
              "29      4\n",
              "44      4\n",
              "48      3\n",
              "47      3\n",
              "36      3\n",
              "45      3\n",
              "34      3\n",
              "41      3\n",
              "35      2\n",
              "12      2\n",
              "40      2\n",
              "26      2\n",
              "31      2\n",
              "43      2\n",
              "38      2\n",
              "24      1\n",
              "33      1\n",
              "22      1\n",
              "21      1\n",
              "27      1\n",
              "16      1\n",
              "23      1\n",
              "28      1\n",
              "37      1\n",
              "Name: product_variation_inventory, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I think the counts of goods in inventroy won't affect the rating."
      ],
      "metadata": {
        "id": "zay69b_80V4l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#shipping_option_name\n",
        "df_train['shipping_option_name'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzu0BWEPxnq9",
        "outputId": "97a31087-d9a6-40d3-cae6-cb3336ff4de9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Livraison standard         1047\n",
              "Standard Shipping            16\n",
              "Envio Padrão                  6\n",
              "الشحن القياسي                 4\n",
              "Expediere Standard            4\n",
              "Envío normal                  3\n",
              "Standardversand               3\n",
              "Livraison Express             3\n",
              "Standardowa wysyłka           2\n",
              "Standart Gönderi              2\n",
              "Стандартная доставка          2\n",
              "Spedizione standard           1\n",
              "ការដឹកជញ្ជូនតាមស្តង់ដារ       1\n",
              "Name: shipping_option_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I think the shipping_option_names are the same but in different languages"
      ],
      "metadata": {
        "id": "IM0WDPR30htC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#shipping_option_price\n",
        "df_train['shipping_option_price'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7sASmGu5u8i",
        "outputId": "758ded39-f70d-472e-d3f5-82b5890f2ad5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     417\n",
              "3     370\n",
              "1     222\n",
              "4      49\n",
              "5      23\n",
              "6       8\n",
              "7       4\n",
              "12      1\n",
              "Name: shipping_option_price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#shipping_is_express\n",
        "df_train['shipping_is_express'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KW5LXWq5u5E",
        "outputId": "bc407a8f-0d01-4aae-90e0-51c0f37fc036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1091\n",
              "1       3\n",
              "Name: shipping_is_express, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#countries_shipped_to\n",
        "df_train['countries_shipped_to'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mEjLMd2e5u2y",
        "outputId": "5c3e1d6e-650f-4eec-daba-5e6c376d86f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41     122\n",
              "43     112\n",
              "40      82\n",
              "38      51\n",
              "36      48\n",
              "      ... \n",
              "107      1\n",
              "111      1\n",
              "56       1\n",
              "135      1\n",
              "53       1\n",
              "Name: countries_shipped_to, Length: 83, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#inventory_total\n",
        "df_train['inventory_total'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hk7zmjpP5u06",
        "outputId": "df74d821-6414-4dc7-fae6-a75940b2d9b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50    1089\n",
              "1        1\n",
              "9        1\n",
              "37       1\n",
              "2        1\n",
              "36       1\n",
              "Name: inventory_total, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the value 50 occures 1089 times and so this featrue won't have effect on the result"
      ],
      "metadata": {
        "id": "gE34XfR31A02"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#has_urgency_banner and urgency_text\n",
        "df_train['has_urgency_banner'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8u_YegK5uyf",
        "outputId": "967e802c-171e-457a-cce9-dc6400e666f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    349\n",
              "Name: has_urgency_banner, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['urgency_text'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYB01p_o5uwD",
        "outputId": "5960085c-c87b-47fd-e8ee-67518d165ee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Quantité limitée !                  348\n",
              "Réduction sur les achats en gros      1\n",
              "Name: urgency_text, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "has_urgency_banner has only one unique value (1)\n",
        "\n",
        "this feature should have 2 unique values (1 and zero)\n",
        "\n",
        "so I thing the nan should be filled with zeros"
      ],
      "metadata": {
        "id": "B7E2bgfQ3yLW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#origin_country\n",
        "df_train['origin_country'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcKBHtoA7S6V",
        "outputId": "90ce2fa8-9320-4516-bad1-16668ed697c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CN    1054\n",
              "US      22\n",
              "VE       3\n",
              "SG       2\n",
              "GB       1\n",
              "Name: origin_country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# merchant_rating_count & merchant_rating\n",
        "df_train['merchant_rating'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2HdM1237S2Z",
        "outputId": "015a99c1-c322-4ed7-b86a-84dfe31bc2ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.045170    8\n",
              "3.884544    7\n",
              "3.867547    6\n",
              "4.080891    6\n",
              "3.908535    6\n",
              "           ..\n",
              "4.069536    1\n",
              "4.316022    1\n",
              "3.837956    1\n",
              "4.256597    1\n",
              "3.748794    1\n",
              "Name: merchant_rating, Length: 742, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['merchant_rating_count'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TpOMk2I17S0K",
        "outputId": "c4c21468-eb13-4002-feb0-e323ecc17a9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12309    8\n",
              "32168    7\n",
              "88193    6\n",
              "55670    6\n",
              "80093    6\n",
              "        ..\n",
              "2946     1\n",
              "2055     1\n",
              "23609    1\n",
              "36       1\n",
              "6632     1\n",
              "Name: merchant_rating_count, Length: 718, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['merchant_rating_count'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsX5Qy8B7SyC",
        "outputId": "b5874853-662e-4222-cb0d-72c1c94667e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.094000e+03\n",
              "mean     2.678399e+04\n",
              "std      8.616171e+04\n",
              "min      4.000000e+00\n",
              "25%      1.926000e+03\n",
              "50%      8.225000e+03\n",
              "75%      2.306200e+04\n",
              "max      2.174765e+06\n",
              "Name: merchant_rating_count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "no merchant_rating_count with zero value"
      ],
      "metadata": {
        "id": "OD2vV9Cz4rFn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#theme\n",
        "df_train['theme'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TllNjY6S7Svj",
        "outputId": "529802d7-3eb9-4e55-ccb9-ad23eff1f052"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "summer    1094\n",
              "Name: theme, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "only one unique value (must be dropped)"
      ],
      "metadata": {
        "id": "1SuqCRnK4vQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#crawl_month\n",
        "df_train['crawl_month'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRELV6o1H1T2",
        "outputId": "7debcfa2-be27-4256-e6a3-2e10d4bd10f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-08    1094\n",
              "Name: crawl_month, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "only one unique value (must be dropped)"
      ],
      "metadata": {
        "id": "JzceKfEt41TS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VR3rgFNU5utz",
        "outputId": "6aaf95a5-7d31-4e1a-ef61-817095c51dc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "no duplicated"
      ],
      "metadata": {
        "id": "ISPfrO-Q42RF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##summary"
      ],
      "metadata": {
        "id": "76dL8Sey44Rx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<pre>\n",
        " 0   price                         \t\n",
        " 1   retail_price                  \t\n",
        " 2   currency_buyer                 drop(only one unique value)\n",
        " 3   units_sold                    \t\n",
        " 4   uses_ad_boosts                \t\n",
        " 5   rating                        \tdrop rows with rate 6\n",
        " 6   rating_count                  \tdrop rows with 0 value\n",
        " 7   badges_count                  \tnot needes as we are going to keep the next three features\n",
        " 8   badge_local_product           \t\n",
        " 9   badge_product_quality         \t\n",
        " 10  badge_fast_shipping           \t\n",
        " 11  tags                           may be preprocessed with one hot encoding or label encoding\n",
        " 12  product_color                 \tmay be preprocessed with one hot encoding or label encoding\n",
        " 13  product_variation_size_id     \tmay be preprocessed with one hot encoding or label encoding\n",
        " 14  product_variation_inventory    drop (not useful to our task)\n",
        " 15  shipping_option_name           drop (same value with different languages)\n",
        " 16  shipping_option_price         \t\n",
        " 17  shipping_is_express           \t\n",
        " 18  countries_shipped_to          \tmay be preprocessed with one hot encoding or label encoding\n",
        " 19  inventory_total               \tdrop (not useful to our task)\n",
        " 20  has_urgency_banner            \tcan fill nan with zeros or dropped\n",
        " 21  urgency_text                  \tdrop (not useful to our task)\n",
        " 22  origin_country                \tmay be preprocessed with one hot encoding or label encoding\n",
        " 23  merchant_title                \tdrop (not useful to our task)\n",
        " 24  merchant_name                 \tdrop (not useful to our task)\n",
        " 25  merchant_info_subtitle        \tdrop (not useful to our task)\n",
        " 26  merchant_rating_count         \tdrop (not useful to our task)\n",
        " 27  merchant_rating               \t\n",
        " 28  merchant_id                   \tdrop (not useful to our task)\n",
        " 29  merchant_has_profile_picture  \tdrop (not useful to our task)\n",
        " 30  merchant_profile_picture      \tdrop (not useful to our task)\n",
        " 31  theme                         \tdrop(only one unique value)\n",
        " 32  crawl_month                   \tdrop(only one unique value)\n",
        " 33  id                            \tdrop (not useful to our task)\n",
        " </pre>"
      ],
      "metadata": {
        "id": "2QemmkG545_G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Clean Data\n",
        "Here we will drop useless column, noisy rows and check duplicates"
      ],
      "metadata": {
        "id": "tLRa566oIKOE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#features to be dropped\n",
        "dropped_columns = ['currency_buyer','badges_count','shipping_option_name',\n",
        "                   'inventory_total','product_variation_inventory','urgency_text',\n",
        "                   'merchant_title','merchant_name','merchant_info_subtitle','merchant_rating_count',\n",
        "                   'merchant_id','merchant_has_profile_picture','merchant_profile_picture','theme','crawl_month','id']\n",
        "dropped_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLIxfFz6H8Nd",
        "outputId": "05d1d2df-0bce-4628-e47b-778300d70557"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['currency_buyer',\n",
              " 'badges_count',\n",
              " 'shipping_option_name',\n",
              " 'inventory_total',\n",
              " 'product_variation_inventory',\n",
              " 'urgency_text',\n",
              " 'merchant_title',\n",
              " 'merchant_name',\n",
              " 'merchant_info_subtitle',\n",
              " 'merchant_rating_count',\n",
              " 'merchant_id',\n",
              " 'merchant_has_profile_picture',\n",
              " 'merchant_profile_picture',\n",
              " 'theme',\n",
              " 'crawl_month',\n",
              " 'id']"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train.drop(dropped_columns, axis=1)\n",
        "df_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "htz2RceEH8LD",
        "outputId": "87b24238-d373-4671-c452-35a284b0f043"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b539ea38-39e8-4e5d-a13f-f9445fe80900\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>retail_price</th>\n",
              "      <th>units_sold</th>\n",
              "      <th>uses_ad_boosts</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>badge_local_product</th>\n",
              "      <th>badge_product_quality</th>\n",
              "      <th>badge_fast_shipping</th>\n",
              "      <th>tags</th>\n",
              "      <th>product_color</th>\n",
              "      <th>product_variation_size_id</th>\n",
              "      <th>shipping_option_price</th>\n",
              "      <th>shipping_is_express</th>\n",
              "      <th>countries_shipped_to</th>\n",
              "      <th>has_urgency_banner</th>\n",
              "      <th>origin_country</th>\n",
              "      <th>merchant_rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.00</td>\n",
              "      <td>7</td>\n",
              "      <td>10000</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1670</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Summer,soildcolor,Plus Size,Tank,camisole,Tops...</td>\n",
              "      <td>yellow</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CN</td>\n",
              "      <td>4.137582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.00</td>\n",
              "      <td>7</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>843</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>bathing suit,Plus Size,bikini set,sexy swimsui...</td>\n",
              "      <td>black</td>\n",
              "      <td>L</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CN</td>\n",
              "      <td>4.256873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.00</td>\n",
              "      <td>34</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Summer,Vest,momshirt,Get,summer t-shirts,funny...</td>\n",
              "      <td>white</td>\n",
              "      <td>L</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CN</td>\n",
              "      <td>3.928920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.67</td>\n",
              "      <td>4</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Summer,Shorts,pants,Beach,Plus Size,beachpant,...</td>\n",
              "      <td>lakeblue</td>\n",
              "      <td>XS</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CN</td>\n",
              "      <td>4.079365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.00</td>\n",
              "      <td>11</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>127</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Summer,Floral print,women dresses,fashion dres...</td>\n",
              "      <td>apricot</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CN</td>\n",
              "      <td>4.306358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>4.00</td>\n",
              "      <td>4</td>\n",
              "      <td>10000</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2150</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>corset top,Fashion,crop top,camisole,knittop,T...</td>\n",
              "      <td>red</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CN</td>\n",
              "      <td>4.311066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>5.00</td>\n",
              "      <td>5</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>377</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Blues,Summer,luggagesticker,Bicycle,cute,fresh...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>H01</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CN</td>\n",
              "      <td>4.422521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1091</th>\n",
              "      <td>9.00</td>\n",
              "      <td>48</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>135</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Summer,Vest,Fashion,Tank,camisole,Halter,Beaut...</td>\n",
              "      <td>purple</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CN</td>\n",
              "      <td>3.748794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1092</th>\n",
              "      <td>11.00</td>\n",
              "      <td>10</td>\n",
              "      <td>10000</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1479</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Summer,Shorts,Cotton,Cotton T Shirt,letter pri...</td>\n",
              "      <td>black</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CN</td>\n",
              "      <td>3.936382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1093</th>\n",
              "      <td>7.00</td>\n",
              "      <td>6</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>sleeve v-neck,Summer,Plus Size,Sleeve,Dresses,...</td>\n",
              "      <td>yellow</td>\n",
              "      <td>XS</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CN</td>\n",
              "      <td>4.045170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1094 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b539ea38-39e8-4e5d-a13f-f9445fe80900')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-b539ea38-39e8-4e5d-a13f-f9445fe80900');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      price  retail_price  ...  origin_country  merchant_rating\n",
              "0      8.00             7  ...              CN         4.137582\n",
              "1      8.00             7  ...              CN         4.256873\n",
              "2      6.00            34  ...              CN         3.928920\n",
              "3      4.67             4  ...              CN         4.079365\n",
              "4      8.00            11  ...              CN         4.306358\n",
              "...     ...           ...  ...             ...              ...\n",
              "1089   4.00             4  ...              CN         4.311066\n",
              "1090   5.00             5  ...              CN         4.422521\n",
              "1091   9.00            48  ...              CN         3.748794\n",
              "1092  11.00            10  ...              CN         3.936382\n",
              "1093   7.00             6  ...              CN         4.045170\n",
              "\n",
              "[1094 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop rows with rating = 6 and rows with rating_count = 0\n",
        "df_train = df_train[df_train['rating'] != 6] \n",
        "df_train = df_train[df_train['rating_count'] != 0]"
      ],
      "metadata": {
        "id": "Cm4aUQTg9czY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#features may be used with one hot encoding or label encoding\n",
        "need_encoding_columns = ['tags','product_color','product_variation_size_id',\n",
        "                         'countries_shipped_to','origin_country']"
      ],
      "metadata": {
        "id": "P5AORpzf8v6S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#features may be filled with values\n",
        "to_fill_columns = ['has_urgency_banner']"
      ],
      "metadata": {
        "id": "Ad1Y9TUT9LiO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhMS5dBXg96r",
        "outputId": "68fd9740-a682-4eff-e869-57bc5ccbb6eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                          0\n",
              "retail_price                   0\n",
              "units_sold                     0\n",
              "uses_ad_boosts                 0\n",
              "rating                         0\n",
              "rating_count                   0\n",
              "badge_local_product            0\n",
              "badge_product_quality          0\n",
              "badge_fast_shipping            0\n",
              "tags                           0\n",
              "product_color                 26\n",
              "product_variation_size_id     12\n",
              "shipping_option_price          0\n",
              "shipping_is_express            0\n",
              "countries_shipped_to           0\n",
              "has_urgency_banner           723\n",
              "origin_country                12\n",
              "merchant_rating                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Trials\n",
        "Steps:\n",
        "1. describing the trial (what we are going to do)\n",
        "2. preprocess the training data\n",
        "3. use gridSearch to try different models\n",
        "4. inspect the results \n",
        "\n",
        "But first we import our models, scalers,... etc\n",
        "\n",
        "And prepare some function to help us."
      ],
      "metadata": {
        "id": "SO_MbcN2ihbM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import cross_val_score, GridSearchCV, StratifiedKFold\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import preprocessing\n",
        "from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "O3g05Cjic03j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def x_y_split(df,y_lables = 'rating'):\n",
        "  return df.iloc[:, df.columns != y_lables] , df[y_lables]"
      ],
      "metadata": {
        "id": "Lr-QcLQgamD0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trial 1 (drop all nominal)\n",
        "* drop all nominal catigorical features (no encoding)\n",
        "* prepare data (drop nominal columns and has_urgency_banner column)\n",
        "* use gridsearch\n",
        "* inspect results \n"
      ],
      "metadata": {
        "id": "8hJQgl9jXHbg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### prepare data"
      ],
      "metadata": {
        "id": "5a-i_gHxYSpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_1 = df_train.copy()"
      ],
      "metadata": {
        "id": "LIEqQC9aZMid"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#drop nominal columns\n",
        "df_trail_1 = df_trail_1.drop(need_encoding_columns,axis = 1)\n",
        "#drop has_urgency_banner \n",
        "df_trail_1 = df_trail_1.drop(to_fill_columns,axis = 1)"
      ],
      "metadata": {
        "id": "mWpvXE6hYZ-_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwE3V_bPZbjb",
        "outputId": "af192c2d-a904-4098-e698-878ba3587c71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1063 entries, 0 to 1093\n",
            "Data columns (total 12 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   price                  1063 non-null   float64\n",
            " 1   retail_price           1063 non-null   int64  \n",
            " 2   units_sold             1063 non-null   int64  \n",
            " 3   uses_ad_boosts         1063 non-null   int64  \n",
            " 4   rating                 1063 non-null   float64\n",
            " 5   rating_count           1063 non-null   int64  \n",
            " 6   badge_local_product    1063 non-null   int64  \n",
            " 7   badge_product_quality  1063 non-null   int64  \n",
            " 8   badge_fast_shipping    1063 non-null   int64  \n",
            " 9   shipping_option_price  1063 non-null   int64  \n",
            " 10  shipping_is_express    1063 non-null   int64  \n",
            " 11  merchant_rating        1063 non-null   float64\n",
            "dtypes: float64(3), int64(9)\n",
            "memory usage: 108.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jw2syWnqa_co",
        "outputId": "dd8710fb-acb1-454d-e8fa-edd34dd442f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                    0\n",
              "retail_price             0\n",
              "units_sold               0\n",
              "uses_ad_boosts           0\n",
              "rating                   0\n",
              "rating_count             0\n",
              "badge_local_product      0\n",
              "badge_product_quality    0\n",
              "badge_fast_shipping      0\n",
              "shipping_option_price    0\n",
              "shipping_is_express      0\n",
              "merchant_rating          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_1_x, df_trail_1_y = x_y_split(df_trail_1)"
      ],
      "metadata": {
        "id": "pQy8nPQoaEnb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_1_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30QEnNV9gRKO",
        "outputId": "8fce675c-993d-4689-a4f3-5fc4d244d065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    774\n",
              "5.0    140\n",
              "3.0    135\n",
              "2.0     12\n",
              "1.0      2\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###GridSearch\n",
        "we will try three models with different parameters:\n",
        "1. SVM\n",
        "2. Decision tree\n",
        "3. GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "hwwyUjlLbYoa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling Data\n",
        "scaler = preprocessing.StandardScaler()\n",
        "df_trail_1_x_scaled = scaler.fit_transform(df_trail_1_x)"
      ],
      "metadata": {
        "id": "Do_GvsoMcoOb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####SVC"
      ],
      "metadata": {
        "id": "1COsi2LEcSNF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#SVC parameters\n",
        "SVC_linear_parametres = {\n",
        "    'C': [0.01],\n",
        "    'kernel': ['linear']\n",
        "}\n",
        "SVC_poly_parametres = {\n",
        "    'C': [0.01],\n",
        "    'degree': [10],\n",
        "    'kernel': ['poly']\n",
        "}\n",
        "SVC_rbf_parametres = {\n",
        "    'C': [0.01, 0.1, 1, 10, 100, 1000,1e5],\n",
        "    'gamma':['scale', 'auto'],\n",
        "    'kernel': ['rbf']\n",
        "}"
      ],
      "metadata": {
        "id": "YBxf477HbzlG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [SVC_linear_parametres , SVC_poly_parametres, SVC_rbf_parametres]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_1_x_scaled,df_trail_1_y)\n",
        "\n",
        "#grid_search with F1-score\n",
        "grid_search = GridSearchCV(SVC(), param_grid = param_list ,\n",
        "                           refit = True, cv = cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_1_x_scaled,df_trail_1_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-c-qGLQNbzRu",
        "outputId": "ced34cb8-2297-4358-d726-06cb2a40119a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce51381a50>,\n",
              "             estimator=SVC(), n_jobs=-1,\n",
              "             param_grid=[{'C': [0.01], 'kernel': ['linear']},\n",
              "                         {'C': [0.01], 'degree': [10], 'kernel': ['poly']},\n",
              "                         {'C': [0.01, 0.1, 1, 10, 100, 1000, 100000.0],\n",
              "                          'gamma': ['scale', 'auto'], 'kernel': ['rbf']}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SVC_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "SVC_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "SVC_resutls[SVC_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "NpXi_UJEfYoG",
        "outputId": "a8299ea3-1e67-47b5-c842-044abb90a20d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-53ea0f98-9de6-42b6-9bdc-e4efb16714c5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
              "      <th>param_kernel</th>\n",
              "      <th>param_degree</th>\n",
              "      <th>param_gamma</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.072762</td>\n",
              "      <td>0.002182</td>\n",
              "      <td>0.015862</td>\n",
              "      <td>0.000776</td>\n",
              "      <td>1</td>\n",
              "      <td>rbf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>scale</td>\n",
              "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.754717</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.749770</td>\n",
              "      <td>0.003104</td>\n",
              "      <td>2</td>\n",
              "      <td>0.770588</td>\n",
              "      <td>0.768235</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.770858</td>\n",
              "      <td>0.763807</td>\n",
              "      <td>0.767639</td>\n",
              "      <td>0.002922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.071698</td>\n",
              "      <td>0.001448</td>\n",
              "      <td>0.016570</td>\n",
              "      <td>0.002183</td>\n",
              "      <td>1</td>\n",
              "      <td>rbf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>auto</td>\n",
              "      <td>{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.754717</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.750709</td>\n",
              "      <td>0.002642</td>\n",
              "      <td>1</td>\n",
              "      <td>0.769412</td>\n",
              "      <td>0.768235</td>\n",
              "      <td>0.763529</td>\n",
              "      <td>0.770858</td>\n",
              "      <td>0.763807</td>\n",
              "      <td>0.767168</td>\n",
              "      <td>0.002977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.093251</td>\n",
              "      <td>0.005730</td>\n",
              "      <td>0.015706</td>\n",
              "      <td>0.000678</td>\n",
              "      <td>10</td>\n",
              "      <td>rbf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>auto</td>\n",
              "      <td>{'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}</td>\n",
              "      <td>0.727700</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.741784</td>\n",
              "      <td>0.726415</td>\n",
              "      <td>0.754717</td>\n",
              "      <td>0.740358</td>\n",
              "      <td>0.011660</td>\n",
              "      <td>3</td>\n",
              "      <td>0.792941</td>\n",
              "      <td>0.795294</td>\n",
              "      <td>0.796471</td>\n",
              "      <td>0.799060</td>\n",
              "      <td>0.790834</td>\n",
              "      <td>0.794920</td>\n",
              "      <td>0.002839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.178851</td>\n",
              "      <td>0.016603</td>\n",
              "      <td>0.016111</td>\n",
              "      <td>0.000518</td>\n",
              "      <td>100</td>\n",
              "      <td>rbf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>scale</td>\n",
              "      <td>{'C': 100, 'gamma': 'scale', 'kernel': 'rbf'}</td>\n",
              "      <td>0.732394</td>\n",
              "      <td>0.741784</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.726415</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.740353</td>\n",
              "      <td>0.009693</td>\n",
              "      <td>4</td>\n",
              "      <td>0.840000</td>\n",
              "      <td>0.837647</td>\n",
              "      <td>0.829412</td>\n",
              "      <td>0.830787</td>\n",
              "      <td>0.830787</td>\n",
              "      <td>0.833727</td>\n",
              "      <td>0.004257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53ea0f98-9de6-42b6-9bdc-e4efb16714c5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-53ea0f98-9de6-42b6-9bdc-e4efb16714c5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-53ea0f98-9de6-42b6-9bdc-e4efb16714c5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "6        0.072762      0.002182  ...          0.767639         0.002922\n",
              "7        0.071698      0.001448  ...          0.767168         0.002977\n",
              "9        0.093251      0.005730  ...          0.794920         0.002839\n",
              "10       0.178851      0.016603  ...          0.833727         0.004257\n",
              "\n",
              "[4 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Decision Tree"
      ],
      "metadata": {
        "id": "Y8t18ouDcYTI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DecisionTreeClassifier()\n",
        "dtreeparam_grid = {\n",
        "    'criterion': ['gini'],\n",
        "    'max_depth': np.arange(7,20),\n",
        "    # 'min_samples_leaf': np.arange(10),\n",
        "    'max_leaf_nodes': np.arange(40,70)\n",
        "}"
      ],
      "metadata": {
        "id": "II7nDHFZeP6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [dtreeparam_grid]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_1_x_scaled,df_trail_1_y)\n",
        "\n",
        "grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_1_x_scaled,df_trail_1_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNhKHQU6eW_C",
        "outputId": "356adae6-8cef-42cc-bc82-78bcb7684dbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 390 candidates, totalling 1950 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce52074cd0>,\n",
              "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'criterion': ['gini'],\n",
              "                          'max_depth': array([ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),\n",
              "                          'max_leaf_nodes': array([40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
              "       57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DecisionTree_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "DecisionTree_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "DecisionTree_resutls[DecisionTree_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "mxBNA1tjhd6k",
        "outputId": "24e1204a-64bf-43eb-ed8e-2adde478a958"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dc62791c-f9ec-4db8-9250-fc90e8c78fcc\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_criterion</th>\n",
              "      <th>param_max_depth</th>\n",
              "      <th>param_max_leaf_nodes</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.006577</td>\n",
              "      <td>0.002977</td>\n",
              "      <td>0.002694</td>\n",
              "      <td>0.000626</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>50</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.779888</td>\n",
              "      <td>0.011292</td>\n",
              "      <td>2</td>\n",
              "      <td>0.841176</td>\n",
              "      <td>0.851765</td>\n",
              "      <td>0.855294</td>\n",
              "      <td>0.846063</td>\n",
              "      <td>0.848414</td>\n",
              "      <td>0.848542</td>\n",
              "      <td>0.004825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.008290</td>\n",
              "      <td>0.002008</td>\n",
              "      <td>0.003667</td>\n",
              "      <td>0.001838</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>54</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.779892</td>\n",
              "      <td>0.013035</td>\n",
              "      <td>1</td>\n",
              "      <td>0.847059</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.857647</td>\n",
              "      <td>0.848414</td>\n",
              "      <td>0.849589</td>\n",
              "      <td>0.851365</td>\n",
              "      <td>0.003936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.010312</td>\n",
              "      <td>0.004165</td>\n",
              "      <td>0.003149</td>\n",
              "      <td>0.001013</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>60</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.778957</td>\n",
              "      <td>0.017209</td>\n",
              "      <td>3</td>\n",
              "      <td>0.849412</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.861176</td>\n",
              "      <td>0.850764</td>\n",
              "      <td>0.850764</td>\n",
              "      <td>0.853247</td>\n",
              "      <td>0.004259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>0.004862</td>\n",
              "      <td>0.000129</td>\n",
              "      <td>0.002150</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>65</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.778953</td>\n",
              "      <td>0.012871</td>\n",
              "      <td>4</td>\n",
              "      <td>0.851765</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.853114</td>\n",
              "      <td>0.851939</td>\n",
              "      <td>0.854187</td>\n",
              "      <td>0.003029</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc62791c-f9ec-4db8-9250-fc90e8c78fcc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dc62791c-f9ec-4db8-9250-fc90e8c78fcc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dc62791c-f9ec-4db8-9250-fc90e8c78fcc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "40       0.006577      0.002977  ...          0.848542         0.004825\n",
              "44       0.008290      0.002008  ...          0.851365         0.003936\n",
              "50       0.010312      0.004165  ...          0.853247         0.004259\n",
              "55       0.004862      0.000129  ...          0.854187         0.003029\n",
              "\n",
              "[4 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####GradientBoosting"
      ],
      "metadata": {
        "id": "_g2ayrHbcX99"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GradientBoostingClassifier()\n",
        "GBC = {\n",
        "    'n_estimators': np.arange(60,150,10),\n",
        "    'learning_rate' : [0.2,0.1,0.01]\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "tGGUVsS6eTiW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [GBC]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_1_x_scaled,df_trail_1_y)\n",
        "\n",
        "grid_search = GridSearchCV(GradientBoostingClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_1_x_scaled,df_trail_1_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vv9mLZgchcLB",
        "outputId": "5aa946ff-564c-4512-baee-692dd12c1616"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce5086a250>,\n",
              "             estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'learning_rate': [0.2, 0.1, 0.01],\n",
              "                          'n_estimators': array([ 60,  70,  80,  90, 100, 110, 120, 130, 140])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBC_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "GBC_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "GBC_resutls[GBC_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "rYhYk41RjVq-",
        "outputId": "6cd828e7-f4ba-49ee-de98-39623e680f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b9d9ca62-a3c2-44e0-9ab6-3b68d50fd05c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_learning_rate</th>\n",
              "      <th>param_n_estimators</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.320368</td>\n",
              "      <td>0.002742</td>\n",
              "      <td>0.005325</td>\n",
              "      <td>0.000149</td>\n",
              "      <td>0.1</td>\n",
              "      <td>90</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 90}</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.820755</td>\n",
              "      <td>0.781792</td>\n",
              "      <td>0.021375</td>\n",
              "      <td>2</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>0.917647</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>0.891892</td>\n",
              "      <td>0.909518</td>\n",
              "      <td>0.907576</td>\n",
              "      <td>0.008461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.613608</td>\n",
              "      <td>0.013246</td>\n",
              "      <td>0.005832</td>\n",
              "      <td>0.000150</td>\n",
              "      <td>0.1</td>\n",
              "      <td>110</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 110}</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.816038</td>\n",
              "      <td>0.780853</td>\n",
              "      <td>0.021139</td>\n",
              "      <td>3</td>\n",
              "      <td>0.925882</td>\n",
              "      <td>0.923529</td>\n",
              "      <td>0.924706</td>\n",
              "      <td>0.908343</td>\n",
              "      <td>0.927145</td>\n",
              "      <td>0.921921</td>\n",
              "      <td>0.006895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.740485</td>\n",
              "      <td>0.006232</td>\n",
              "      <td>0.006173</td>\n",
              "      <td>0.000133</td>\n",
              "      <td>0.1</td>\n",
              "      <td>120</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 120}</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.820755</td>\n",
              "      <td>0.779910</td>\n",
              "      <td>0.022937</td>\n",
              "      <td>4</td>\n",
              "      <td>0.931765</td>\n",
              "      <td>0.929412</td>\n",
              "      <td>0.932941</td>\n",
              "      <td>0.916569</td>\n",
              "      <td>0.931845</td>\n",
              "      <td>0.928506</td>\n",
              "      <td>0.006079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.900988</td>\n",
              "      <td>0.015074</td>\n",
              "      <td>0.006392</td>\n",
              "      <td>0.000146</td>\n",
              "      <td>0.1</td>\n",
              "      <td>130</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 130}</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.825472</td>\n",
              "      <td>0.782740</td>\n",
              "      <td>0.025083</td>\n",
              "      <td>1</td>\n",
              "      <td>0.936471</td>\n",
              "      <td>0.934118</td>\n",
              "      <td>0.941176</td>\n",
              "      <td>0.924794</td>\n",
              "      <td>0.940071</td>\n",
              "      <td>0.935326</td>\n",
              "      <td>0.005838</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9d9ca62-a3c2-44e0-9ab6-3b68d50fd05c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b9d9ca62-a3c2-44e0-9ab6-3b68d50fd05c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b9d9ca62-a3c2-44e0-9ab6-3b68d50fd05c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "12       1.320368      0.002742  ...          0.907576         0.008461\n",
              "14       1.613608      0.013246  ...          0.921921         0.006895\n",
              "15       1.740485      0.006232  ...          0.928506         0.006079\n",
              "16       1.900988      0.015074  ...          0.935326         0.005838\n",
              "\n",
              "[4 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Inspect results"
      ],
      "metadata": {
        "id": "G9ULKlKSlEc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('SVC best f1-score:{0} , best prams:{1}'.format(SVC_best[0],SVC_best[1]))\n",
        "print('DT best f1-score:{0} , best prams:{1}'.format(DecisionTree_best[0],DecisionTree_best[1]))\n",
        "print('GBC best f1-score:{0} , best prams:{1}'.format(GBC_best[0],GBC_best[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rapRF30plZj-",
        "outputId": "92d3981a-747f-448f-b97d-ad2d9d7ca89f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC best f1-score:0.7507086544423774 , best prams:{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}\n",
            "DT best f1-score:0.7798919301975374 , best prams:{'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 54}\n",
            "GBC best f1-score:0.7827398352378422 , best prams:{'learning_rate': 0.1, 'n_estimators': 130}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "not bad for the first trial\n",
        "\n",
        "the best model is GBC with mean test score = 0.78367"
      ],
      "metadata": {
        "id": "PBKJE0rzv71r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Trial 2 (add has_urgency_banner)\n",
        "* the same as trial one but we will add has_urgency_banner column and replace null values with zero."
      ],
      "metadata": {
        "id": "HoqXxL0Su7uw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###prepare data"
      ],
      "metadata": {
        "id": "Z2DaDyNWsePi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_2 = df_train.copy()"
      ],
      "metadata": {
        "id": "_fX-zjyLs7_t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#drop nominal columns\n",
        "df_trail_2 = df_trail_2.drop(need_encoding_columns,axis = 1)"
      ],
      "metadata": {
        "id": "sY6CIgsQtFok"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_2.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjGRdrzYtKRc",
        "outputId": "a2b3ced3-fe45-4c8a-ccf7-2c169f8b8756"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                      0\n",
              "retail_price               0\n",
              "units_sold                 0\n",
              "uses_ad_boosts             0\n",
              "rating                     0\n",
              "rating_count               0\n",
              "badge_local_product        0\n",
              "badge_product_quality      0\n",
              "badge_fast_shipping        0\n",
              "shipping_option_price      0\n",
              "shipping_is_express        0\n",
              "has_urgency_banner       723\n",
              "merchant_rating            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_2.fillna(0,inplace = True)"
      ],
      "metadata": {
        "id": "hJLDy099tOhZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_2.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nELI0cC6tUJr",
        "outputId": "28ca4ae6-639c-46ed-c8bb-49a5825b229d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                    0\n",
              "retail_price             0\n",
              "units_sold               0\n",
              "uses_ad_boosts           0\n",
              "rating                   0\n",
              "rating_count             0\n",
              "badge_local_product      0\n",
              "badge_product_quality    0\n",
              "badge_fast_shipping      0\n",
              "shipping_option_price    0\n",
              "shipping_is_express      0\n",
              "has_urgency_banner       0\n",
              "merchant_rating          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_2_x, df_trail_2_y = x_y_split(df_trail_2)"
      ],
      "metadata": {
        "id": "BXRfivIHtquK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###GridSearch\n",
        "we will try three models with different parameters:\n",
        "1. SVM\n",
        "2. Decision tree\n",
        "3. GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "kjZV-QXhtfvu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling Data\n",
        "scaler = preprocessing.StandardScaler()\n",
        "df_trail_2_x_scaled = scaler.fit_transform(df_trail_2_x)"
      ],
      "metadata": {
        "id": "YUbmZY5btfvv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####SVC"
      ],
      "metadata": {
        "id": "2DxTxXt3tfvv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#SVC parameters\n",
        "SVC_linear_parametres = {\n",
        "    'C': [0.01],\n",
        "    'kernel': ['linear']\n",
        "}\n",
        "SVC_poly_parametres = {\n",
        "    'C': [0.01],\n",
        "    'degree': [10],\n",
        "    'kernel': ['poly']\n",
        "}\n",
        "SVC_rbf_parametres = {\n",
        "    'C': [0.01, 0.1, 1, 10, 100, 1000,1e5],\n",
        "    'gamma':['scale', 'auto'],\n",
        "    'kernel': ['rbf']\n",
        "}"
      ],
      "metadata": {
        "id": "ZJCqa1Pltfvv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [SVC_linear_parametres , SVC_poly_parametres, SVC_rbf_parametres]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_2_x_scaled,df_trail_2_y)\n",
        "\n",
        "#grid_search with F1-score\n",
        "grid_search = GridSearchCV(SVC(), param_grid = param_list ,\n",
        "                           refit = True, cv = cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_2_x_scaled,df_trail_2_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d421c0a8-f8f7-46d6-bcbc-122dae178b37",
        "id": "Yr8IRUpetfvv"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce5086aed0>,\n",
              "             estimator=SVC(), n_jobs=-1,\n",
              "             param_grid=[{'C': [0.01], 'kernel': ['linear']},\n",
              "                         {'C': [0.01], 'degree': [10], 'kernel': ['poly']},\n",
              "                         {'C': [0.01, 0.1, 1, 10, 100, 1000, 100000.0],\n",
              "                          'gamma': ['scale', 'auto'], 'kernel': ['rbf']}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SVC_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "SVC_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "SVC_resutls[SVC_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "3b06e7bd-bcfa-499f-e2c7-831deb5c4e6f",
        "id": "hxEIxW_Ktfvw"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
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              "      <th>param_gamma</th>\n",
              "      <th>params</th>\n",
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              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
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              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.073298</td>\n",
              "      <td>0.004082</td>\n",
              "      <td>0.016173</td>\n",
              "      <td>0.000300</td>\n",
              "      <td>1</td>\n",
              "      <td>rbf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>scale</td>\n",
              "      <td>{'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}</td>\n",
              "      <td>0.732394</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.745283</td>\n",
              "      <td>0.754717</td>\n",
              "      <td>0.746009</td>\n",
              "      <td>0.007597</td>\n",
              "      <td>2</td>\n",
              "      <td>0.771765</td>\n",
              "      <td>0.771765</td>\n",
              "      <td>0.768235</td>\n",
              "      <td>0.770858</td>\n",
              "      <td>0.768508</td>\n",
              "      <td>0.770226</td>\n",
              "      <td>0.001552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.073871</td>\n",
              "      <td>0.003138</td>\n",
              "      <td>0.016589</td>\n",
              "      <td>0.000920</td>\n",
              "      <td>1</td>\n",
              "      <td>rbf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>auto</td>\n",
              "      <td>{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}</td>\n",
              "      <td>0.732394</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.745283</td>\n",
              "      <td>0.759434</td>\n",
              "      <td>0.746953</td>\n",
              "      <td>0.008815</td>\n",
              "      <td>1</td>\n",
              "      <td>0.772941</td>\n",
              "      <td>0.771765</td>\n",
              "      <td>0.768235</td>\n",
              "      <td>0.770858</td>\n",
              "      <td>0.768508</td>\n",
              "      <td>0.770461</td>\n",
              "      <td>0.001832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.095777</td>\n",
              "      <td>0.008375</td>\n",
              "      <td>0.018669</td>\n",
              "      <td>0.002709</td>\n",
              "      <td>10</td>\n",
              "      <td>rbf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>scale</td>\n",
              "      <td>{'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}</td>\n",
              "      <td>0.723005</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.737089</td>\n",
              "      <td>0.731132</td>\n",
              "      <td>0.754717</td>\n",
              "      <td>0.740362</td>\n",
              "      <td>0.012990</td>\n",
              "      <td>3</td>\n",
              "      <td>0.801176</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.801176</td>\n",
              "      <td>0.797885</td>\n",
              "      <td>0.794360</td>\n",
              "      <td>0.798919</td>\n",
              "      <td>0.002578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.093663</td>\n",
              "      <td>0.003605</td>\n",
              "      <td>0.016986</td>\n",
              "      <td>0.001494</td>\n",
              "      <td>10</td>\n",
              "      <td>rbf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>auto</td>\n",
              "      <td>{'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}</td>\n",
              "      <td>0.723005</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.732394</td>\n",
              "      <td>0.731132</td>\n",
              "      <td>0.754717</td>\n",
              "      <td>0.739423</td>\n",
              "      <td>0.013357</td>\n",
              "      <td>4</td>\n",
              "      <td>0.801176</td>\n",
              "      <td>0.801176</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.797885</td>\n",
              "      <td>0.793184</td>\n",
              "      <td>0.798684</td>\n",
              "      <td>0.003001</td>\n",
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              "          const element = document.querySelector('#df-66cfc87c-d6d5-4559-bbb0-0677ebaad601');\n",
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              "  "
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "6       0.073298      0.004082  ...          0.770226         0.001552\n",
              "7       0.073871      0.003138  ...          0.770461         0.001832\n",
              "8       0.095777      0.008375  ...          0.798919         0.002578\n",
              "9       0.093663      0.003605  ...          0.798684         0.003001\n",
              "\n",
              "[4 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Decision Tree"
      ],
      "metadata": {
        "id": "URXBa2Witfvw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DecisionTreeClassifier()\n",
        "dtreeparam_grid = {\n",
        "    'criterion': ['gini'],\n",
        "    'max_depth': np.arange(7,20),\n",
        "    # 'min_samples_leaf': np.arange(10),\n",
        "    'max_leaf_nodes': np.arange(40,70)\n",
        "}"
      ],
      "metadata": {
        "id": "ChtqEkoYtfvw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [dtreeparam_grid]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_2_x_scaled,df_trail_2_y)\n",
        "\n",
        "grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_2_x_scaled,df_trail_2_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32a78390-3cf5-4508-bb61-afa4424114f8",
        "id": "XHfDdSQ_tfvw"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 390 candidates, totalling 1950 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce5086af50>,\n",
              "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'criterion': ['gini'],\n",
              "                          'max_depth': array([ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),\n",
              "                          'max_leaf_nodes': array([40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
              "       57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DecisionTree_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "DecisionTree_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "DecisionTree_resutls[DecisionTree_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "d23e55e5-4949-45c1-9a4c-a4022d69238d",
        "id": "4Q6I-LP2tfvw"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
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              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
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              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.004997</td>\n",
              "      <td>0.000186</td>\n",
              "      <td>0.002090</td>\n",
              "      <td>0.000081</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>48</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.776118</td>\n",
              "      <td>0.009359</td>\n",
              "      <td>3</td>\n",
              "      <td>0.840000</td>\n",
              "      <td>0.851765</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.846063</td>\n",
              "      <td>0.846063</td>\n",
              "      <td>0.847602</td>\n",
              "      <td>0.004946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.004623</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.001847</td>\n",
              "      <td>0.000087</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>49</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.776123</td>\n",
              "      <td>0.009726</td>\n",
              "      <td>2</td>\n",
              "      <td>0.841176</td>\n",
              "      <td>0.851765</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.846063</td>\n",
              "      <td>0.847239</td>\n",
              "      <td>0.848072</td>\n",
              "      <td>0.004527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.005891</td>\n",
              "      <td>0.001724</td>\n",
              "      <td>0.002784</td>\n",
              "      <td>0.001532</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>54</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.778302</td>\n",
              "      <td>0.775175</td>\n",
              "      <td>0.008325</td>\n",
              "      <td>4</td>\n",
              "      <td>0.847059</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.857647</td>\n",
              "      <td>0.848414</td>\n",
              "      <td>0.849589</td>\n",
              "      <td>0.851365</td>\n",
              "      <td>0.003936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.004979</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.002311</td>\n",
              "      <td>0.000560</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>67</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.776127</td>\n",
              "      <td>0.010923</td>\n",
              "      <td>1</td>\n",
              "      <td>0.851765</td>\n",
              "      <td>0.855294</td>\n",
              "      <td>0.858824</td>\n",
              "      <td>0.853114</td>\n",
              "      <td>0.851939</td>\n",
              "      <td>0.854187</td>\n",
              "      <td>0.002638</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e306297-7400-4f5d-8dd8-2c7b5262844e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e306297-7400-4f5d-8dd8-2c7b5262844e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1e306297-7400-4f5d-8dd8-2c7b5262844e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "38       0.004997      0.000186  ...          0.847602         0.004946\n",
              "39       0.004623      0.000072  ...          0.848072         0.004527\n",
              "44       0.005891      0.001724  ...          0.851365         0.003936\n",
              "57       0.004979      0.000131  ...          0.854187         0.002638\n",
              "\n",
              "[4 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####GradientBoosting"
      ],
      "metadata": {
        "id": "5PrDhqaPtfvx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GradientBoostingClassifier()\n",
        "GBC = {\n",
        "    'n_estimators': np.arange(60,150,10),\n",
        "    'learning_rate' : [0.2,0.1,0.01]\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "tDnX7itztfvx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [GBC]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_2_x_scaled,df_trail_2_y)\n",
        "\n",
        "grid_search = GridSearchCV(GradientBoostingClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_2_x_scaled,df_trail_2_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c873912-cb58-49da-a73c-e67ea98fac5c",
        "id": "hV2aBvs-tfvy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce50804450>,\n",
              "             estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'learning_rate': [0.2, 0.1, 0.01],\n",
              "                          'n_estimators': array([ 60,  70,  80,  90, 100, 110, 120, 130, 140])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBC_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "GBC_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "GBC_resutls[GBC_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "0e4839d1-6ac3-4a05-cabd-3b165934197b",
        "id": "rY9oMgq4tfvy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-459555e0-ec40-491d-8bb3-986fc29afb1c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_learning_rate</th>\n",
              "      <th>param_n_estimators</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.929674</td>\n",
              "      <td>0.010277</td>\n",
              "      <td>0.006808</td>\n",
              "      <td>0.000876</td>\n",
              "      <td>0.2</td>\n",
              "      <td>140</td>\n",
              "      <td>{'learning_rate': 0.2, 'n_estimators': 140}</td>\n",
              "      <td>0.737089</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.776140</td>\n",
              "      <td>0.021193</td>\n",
              "      <td>4</td>\n",
              "      <td>0.997647</td>\n",
              "      <td>0.995294</td>\n",
              "      <td>0.994118</td>\n",
              "      <td>0.992949</td>\n",
              "      <td>0.995300</td>\n",
              "      <td>0.995062</td>\n",
              "      <td>0.001559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.191305</td>\n",
              "      <td>0.012165</td>\n",
              "      <td>0.005054</td>\n",
              "      <td>0.000165</td>\n",
              "      <td>0.1</td>\n",
              "      <td>80</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 80}</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.776140</td>\n",
              "      <td>0.017567</td>\n",
              "      <td>4</td>\n",
              "      <td>0.905882</td>\n",
              "      <td>0.901176</td>\n",
              "      <td>0.895294</td>\n",
              "      <td>0.893067</td>\n",
              "      <td>0.896592</td>\n",
              "      <td>0.898402</td>\n",
              "      <td>0.004584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.341717</td>\n",
              "      <td>0.013653</td>\n",
              "      <td>0.005248</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.1</td>\n",
              "      <td>90</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 90}</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.778023</td>\n",
              "      <td>0.019865</td>\n",
              "      <td>2</td>\n",
              "      <td>0.916471</td>\n",
              "      <td>0.914118</td>\n",
              "      <td>0.908235</td>\n",
              "      <td>0.901293</td>\n",
              "      <td>0.908343</td>\n",
              "      <td>0.909692</td>\n",
              "      <td>0.005292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.475871</td>\n",
              "      <td>0.006835</td>\n",
              "      <td>0.007798</td>\n",
              "      <td>0.003999</td>\n",
              "      <td>0.1</td>\n",
              "      <td>100</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.779901</td>\n",
              "      <td>0.019453</td>\n",
              "      <td>1</td>\n",
              "      <td>0.928235</td>\n",
              "      <td>0.917647</td>\n",
              "      <td>0.917647</td>\n",
              "      <td>0.907168</td>\n",
              "      <td>0.917744</td>\n",
              "      <td>0.917688</td>\n",
              "      <td>0.006662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.630704</td>\n",
              "      <td>0.028258</td>\n",
              "      <td>0.007082</td>\n",
              "      <td>0.002176</td>\n",
              "      <td>0.1</td>\n",
              "      <td>110</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 110}</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.778023</td>\n",
              "      <td>0.019642</td>\n",
              "      <td>2</td>\n",
              "      <td>0.931765</td>\n",
              "      <td>0.922353</td>\n",
              "      <td>0.924706</td>\n",
              "      <td>0.918919</td>\n",
              "      <td>0.927145</td>\n",
              "      <td>0.924977</td>\n",
              "      <td>0.004346</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-459555e0-ec40-491d-8bb3-986fc29afb1c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-459555e0-ec40-491d-8bb3-986fc29afb1c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-459555e0-ec40-491d-8bb3-986fc29afb1c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "8        1.929674      0.010277  ...          0.995062         0.001559\n",
              "11       1.191305      0.012165  ...          0.898402         0.004584\n",
              "12       1.341717      0.013653  ...          0.909692         0.005292\n",
              "13       1.475871      0.006835  ...          0.917688         0.006662\n",
              "14       1.630704      0.028258  ...          0.924977         0.004346\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Inspect results"
      ],
      "metadata": {
        "id": "izeB-55Ysxqm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('SVC best f1-score:{0} , best prams:{1}'.format(SVC_best[0],SVC_best[1]))\n",
        "print('DT best f1-score:{0} , best prams:{1}'.format(DecisionTree_best[0],DecisionTree_best[1]))\n",
        "print('GBC best f1-score:{0} , best prams:{1}'.format(GBC_best[0],GBC_best[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8NsrfyO2t1aR",
        "outputId": "dc81bb4b-975c-4951-e984-e74db8b873e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC best f1-score:0.7469527858977767 , best prams:{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}\n",
            "DT best f1-score:0.7761272034724069 , best prams:{'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 67}\n",
            "GBC best f1-score:0.7799007883780671 , best prams:{'learning_rate': 0.1, 'n_estimators': 100}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The F1-score decreased so for the upcomming trials we will drop has_urgency_banner column"
      ],
      "metadata": {
        "id": "kCxtlvgRvtwp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trial 3 (add tage column)\n",
        "* prepare tage column \n",
        "* prepare data (drop nominal columns and has_urgency_banner column then add tag column)\n",
        "* use gridsearch (DT and GB only)\n",
        "* inspect results \n"
      ],
      "metadata": {
        "id": "IDYSQAZB45Wu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###prepare tage column"
      ],
      "metadata": {
        "id": "BMM1OgbW5Wb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tags_column = df_train['tags']"
      ],
      "metadata": {
        "id": "GaBfmbKX5iNu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tags_column.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88e561cb-31af-4c27-e831-d86c3e856c88",
        "id": "AEn4LK0n5iNv"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tags_df = tags_column.str.get_dummies(',')\n",
        "tags_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "39d27c81-2b46-435b-8919-3411a3bb34e3",
        "id": "kB8iiNP75iNw"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42f6cd36-0921-4af2-88bf-16de1cde1a91')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-42f6cd36-0921-4af2-88bf-16de1cde1a91 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-42f6cd36-0921-4af2-88bf-16de1cde1a91');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      #Sport Bra  #fashion #shorts  ...  zippersleevelessvest  漁師の帽子\n",
              "0              0                 0  ...                     0      0\n",
              "1              0                 0  ...                     0      0\n",
              "2              0                 0  ...                     0      0\n",
              "3              0                 0  ...                     0      0\n",
              "4              0                 0  ...                     0      0\n",
              "...          ...               ...  ...                   ...    ...\n",
              "1089           0                 0  ...                     0      0\n",
              "1090           0                 0  ...                     0      0\n",
              "1091           0                 0  ...                     0      0\n",
              "1092           0                 0  ...                     0      0\n",
              "1093           0                 0  ...                     0      0\n",
              "\n",
              "[1063 rows x 2136 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the result contains 2136 which is a huge number\n",
        "we will reduce this number in another tiral."
      ],
      "metadata": {
        "id": "EjVp50nQ6rtz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### prepare data"
      ],
      "metadata": {
        "id": "j5arcY_r45Wv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_3 = df_train.copy()"
      ],
      "metadata": {
        "id": "BzvRX9yK45Wv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#drop nominal columns\n",
        "df_trail_3 = df_trail_3.drop(need_encoding_columns,axis = 1)\n",
        "#drop has_urgency_banner \n",
        "df_trail_3 = df_trail_3.drop(to_fill_columns,axis = 1)"
      ],
      "metadata": {
        "id": "JLFVkv9U45Ww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add tage encodes df\n",
        "df_trail_3 = pd.concat([df_trail_3, tags_df], axis=1)"
      ],
      "metadata": {
        "id": "2iwg4Cuv60Vv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_3.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4c4f950-ea4d-4587-e4a0-12c61e1a763d",
        "id": "tvPBDwXQ45Wx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1063 entries, 0 to 1093\n",
            "Columns: 2148 entries, price to 漁師の帽子\n",
            "dtypes: float64(3), int64(2145)\n",
            "memory usage: 17.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_3.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0ba91ca-b729-4441-ee8a-04bddb297d90",
        "id": "sX7YtVCe45Wy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_3_x, df_trail_3_y = x_y_split(df_trail_3)"
      ],
      "metadata": {
        "id": "9EEUnR6L45Wz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_3_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e92c812-c79b-4fe7-9601-934e7fecea0c",
        "id": "MACsrm__45W0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    774\n",
              "5.0    140\n",
              "3.0    135\n",
              "2.0     12\n",
              "1.0      2\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###GridSearch\n",
        "we will try three models with different parameters:\n",
        "1. Decision tree\n",
        "2. GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "sgucejIR45W0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling Data\n",
        "scaler = preprocessing.StandardScaler()\n",
        "df_trail_3_x_scaled = scaler.fit_transform(df_trail_3_x)"
      ],
      "metadata": {
        "id": "aR2Q3zir45W0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Decision Tree"
      ],
      "metadata": {
        "id": "cB1CqmYr45W2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DecisionTreeClassifier()\n",
        "dtreeparam_grid = {\n",
        "    'criterion': ['gini'],\n",
        "    'max_depth': np.arange(7,20),\n",
        "    # 'min_samples_leaf': np.arange(10),\n",
        "    'max_leaf_nodes': np.arange(40,70)\n",
        "}"
      ],
      "metadata": {
        "id": "UUpElHmg45W2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [dtreeparam_grid]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_3_x_scaled,df_trail_3_y)\n",
        "\n",
        "grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_3_x_scaled,df_trail_3_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c0127f1-29ef-4603-f155-60a312da76af",
        "id": "LwxAy2rO45W2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 390 candidates, totalling 1950 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce508044d0>,\n",
              "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'criterion': ['gini'],\n",
              "                          'max_depth': array([ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),\n",
              "                          'max_leaf_nodes': array([40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
              "       57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DecisionTree_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "DecisionTree_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "DecisionTree_resutls[DecisionTree_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "ee4eda54-5220-4d06-afcb-50f70a50fd18",
        "id": "MTxXYZg_45W3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
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              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>0.189705</td>\n",
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              "      <td>0.000152</td>\n",
              "      <td>gini</td>\n",
              "      <td>10</td>\n",
              "      <td>47</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 10, 'max_le...</td>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.793427</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.834906</td>\n",
              "      <td>0.801887</td>\n",
              "      <td>0.794025</td>\n",
              "      <td>0.026824</td>\n",
              "      <td>1</td>\n",
              "      <td>0.876471</td>\n",
              "      <td>0.875294</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.873090</td>\n",
              "      <td>0.877791</td>\n",
              "      <td>0.876529</td>\n",
              "      <td>0.002323</td>\n",
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              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>0.188296</td>\n",
              "      <td>0.006688</td>\n",
              "      <td>0.004381</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>gini</td>\n",
              "      <td>10</td>\n",
              "      <td>54</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 10, 'max_le...</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.830189</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.792147</td>\n",
              "      <td>0.023457</td>\n",
              "      <td>4</td>\n",
              "      <td>0.885882</td>\n",
              "      <td>0.883529</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.881316</td>\n",
              "      <td>0.887192</td>\n",
              "      <td>0.883584</td>\n",
              "      <td>0.002694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>0.184141</td>\n",
              "      <td>0.004149</td>\n",
              "      <td>0.004407</td>\n",
              "      <td>0.000188</td>\n",
              "      <td>gini</td>\n",
              "      <td>10</td>\n",
              "      <td>61</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 10, 'max_le...</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.834906</td>\n",
              "      <td>0.811321</td>\n",
              "      <td>0.793095</td>\n",
              "      <td>0.027340</td>\n",
              "      <td>2</td>\n",
              "      <td>0.891765</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.881176</td>\n",
              "      <td>0.889542</td>\n",
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              "      <td>0.886640</td>\n",
              "      <td>0.004145</td>\n",
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              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>0.202328</td>\n",
              "      <td>0.010667</td>\n",
              "      <td>0.004408</td>\n",
              "      <td>0.000084</td>\n",
              "      <td>gini</td>\n",
              "      <td>10</td>\n",
              "      <td>66</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 10, 'max_le...</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.839623</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.793086</td>\n",
              "      <td>0.026260</td>\n",
              "      <td>3</td>\n",
              "      <td>0.891765</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.889542</td>\n",
              "      <td>0.888367</td>\n",
              "      <td>0.886405</td>\n",
              "      <td>0.004469</td>\n",
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              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>0.200268</td>\n",
              "      <td>0.010059</td>\n",
              "      <td>0.004391</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>gini</td>\n",
              "      <td>10</td>\n",
              "      <td>67</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 10, 'max_le...</td>\n",
              "      <td>0.779343</td>\n",
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              "      <td>0.769953</td>\n",
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              "      <td>0.806604</td>\n",
              "      <td>0.792147</td>\n",
              "      <td>0.022886</td>\n",
              "      <td>4</td>\n",
              "      <td>0.891765</td>\n",
              "      <td>0.890588</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.889542</td>\n",
              "      <td>0.888367</td>\n",
              "      <td>0.888052</td>\n",
              "      <td>0.004180</td>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e93ae8f4-6ac0-465e-b524-e4b7446aa0b7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e93ae8f4-6ac0-465e-b524-e4b7446aa0b7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "97        0.189705      0.006571  ...          0.876529         0.002323\n",
              "104       0.188296      0.006688  ...          0.883584         0.002694\n",
              "111       0.184141      0.004149  ...          0.886640         0.004145\n",
              "116       0.202328      0.010667  ...          0.886405         0.004469\n",
              "117       0.200268      0.010059  ...          0.888052         0.004180\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####GradientBoosting"
      ],
      "metadata": {
        "id": "9yULeYyI45W3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GradientBoostingClassifier()\n",
        "GBC = {\n",
        "    'n_estimators': np.arange(70,130,10),\n",
        "    'learning_rate' : [0.2,0.1,0.01]\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "WNxs0tFV45W3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [GBC]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_3_x_scaled,df_trail_3_y)\n",
        "\n",
        "grid_search = GridSearchCV(GradientBoostingClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_3_x_scaled,df_trail_3_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f968ec5e-ff96-4d00-d2cc-b70944b36c2f",
        "id": "kI85TEPr45W3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce50804ad0>,\n",
              "             estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'learning_rate': [0.2, 0.1, 0.01],\n",
              "                          'n_estimators': array([ 70,  80,  90, 100, 110, 120])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBC_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "GBC_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "GBC_resutls[GBC_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "0a5d66bd-4ea4-4a34-f94a-ef0838387f47",
        "id": "5Ms0h0wq45W4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_learning_rate</th>\n",
              "      <th>param_n_estimators</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19.158426</td>\n",
              "      <td>0.416089</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.000787</td>\n",
              "      <td>0.2</td>\n",
              "      <td>70</td>\n",
              "      <td>{'learning_rate': 0.2, 'n_estimators': 70}</td>\n",
              "      <td>0.793427</td>\n",
              "      <td>0.802817</td>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.811321</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.800580</td>\n",
              "      <td>0.008345</td>\n",
              "      <td>1</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.962353</td>\n",
              "      <td>0.964706</td>\n",
              "      <td>0.954172</td>\n",
              "      <td>0.952996</td>\n",
              "      <td>0.958845</td>\n",
              "      <td>0.004561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>24.983955</td>\n",
              "      <td>0.590838</td>\n",
              "      <td>0.008942</td>\n",
              "      <td>0.000648</td>\n",
              "      <td>0.2</td>\n",
              "      <td>90</td>\n",
              "      <td>{'learning_rate': 0.2, 'n_estimators': 90}</td>\n",
              "      <td>0.793427</td>\n",
              "      <td>0.816901</td>\n",
              "      <td>0.793427</td>\n",
              "      <td>0.811321</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.800562</td>\n",
              "      <td>0.011393</td>\n",
              "      <td>2</td>\n",
              "      <td>0.968235</td>\n",
              "      <td>0.969412</td>\n",
              "      <td>0.974118</td>\n",
              "      <td>0.964747</td>\n",
              "      <td>0.963572</td>\n",
              "      <td>0.968017</td>\n",
              "      <td>0.003732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27.921983</td>\n",
              "      <td>0.761626</td>\n",
              "      <td>0.009223</td>\n",
              "      <td>0.000500</td>\n",
              "      <td>0.2</td>\n",
              "      <td>100</td>\n",
              "      <td>{'learning_rate': 0.2, 'n_estimators': 100}</td>\n",
              "      <td>0.802817</td>\n",
              "      <td>0.798122</td>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.811321</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.799632</td>\n",
              "      <td>0.007402</td>\n",
              "      <td>3</td>\n",
              "      <td>0.978824</td>\n",
              "      <td>0.971765</td>\n",
              "      <td>0.976471</td>\n",
              "      <td>0.962397</td>\n",
              "      <td>0.965922</td>\n",
              "      <td>0.971076</td>\n",
              "      <td>0.006188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.790869</td>\n",
              "      <td>0.796178</td>\n",
              "      <td>0.009693</td>\n",
              "      <td>0.000540</td>\n",
              "      <td>0.2</td>\n",
              "      <td>110</td>\n",
              "      <td>{'learning_rate': 0.2, 'n_estimators': 110}</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.812207</td>\n",
              "      <td>0.798122</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.798685</td>\n",
              "      <td>0.009992</td>\n",
              "      <td>4</td>\n",
              "      <td>0.981176</td>\n",
              "      <td>0.975294</td>\n",
              "      <td>0.978824</td>\n",
              "      <td>0.971798</td>\n",
              "      <td>0.968273</td>\n",
              "      <td>0.975073</td>\n",
              "      <td>0.004655</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0158c32-fb86-4577-a91c-8482039e4789')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a0158c32-fb86-4577-a91c-8482039e4789 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a0158c32-fb86-4577-a91c-8482039e4789');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0      19.158426      0.416089  ...          0.958845         0.004561\n",
              "2      24.983955      0.590838  ...          0.968017         0.003732\n",
              "3      27.921983      0.761626  ...          0.971076         0.006188\n",
              "4      30.790869      0.796178  ...          0.975073         0.004655\n",
              "\n",
              "[4 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Inspect results"
      ],
      "metadata": {
        "id": "jEIhX60M45W4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('DT best f1-score:{0} , best prams:{1}'.format(DecisionTree_best[0],DecisionTree_best[1]))\n",
        "print('GBC best f1-score:{0} , best prams:{1}'.format(GBC_best[0],GBC_best[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d33cccc6-7232-4b71-d2f6-066f447fc9e4",
        "id": "6oMkfyfp45W4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DT best f1-score:0.7940251572327043 , best prams:{'criterion': 'gini', 'max_depth': 10, 'max_leaf_nodes': 47}\n",
            "GBC best f1-score:0.8005802108246967 , best prams:{'learning_rate': 0.2, 'n_estimators': 70}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "F1-score is heigher than befor\n",
        "\n",
        "next we reduce the number of columns in tag df"
      ],
      "metadata": {
        "id": "LtOcxCOCHn2J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "uZXPqKaWH1td"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trial 4 (reduce tage column)\n",
        "* reduce tags column\n",
        "* prepare data (drop nominal columns and has_urgency_banner column then add tag column)\n",
        "* use gridsearch (DT and GB only)\n",
        "* inspect results \n"
      ],
      "metadata": {
        "id": "n5d8YWN_H24u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###reduce tage column"
      ],
      "metadata": {
        "id": "bWLMyjoBH24v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tags_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "K9RBy7f4H24w",
        "outputId": "5a251915-87fe-409f-c389-b5def4730335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>#Sport Bra</th>\n",
              "      <th>#fashion #shorts</th>\n",
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              "      <th>#top #crop</th>\n",
              "      <th>2 Piece Swimsuit</th>\n",
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              "      <th>2014menshort</th>\n",
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              "      <th>2in1short</th>\n",
              "      <th>2in1sportsjogging</th>\n",
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              "      <th>2piecesbikini</th>\n",
              "      <th>2pieceset</th>\n",
              "      <th>34pant</th>\n",
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              "      <th>3D T-shirt</th>\n",
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              "      <th>3dprinttshirt</th>\n",
              "      <th>3mreflective</th>\n",
              "      <th>A line dress</th>\n",
              "      <th>A-line</th>\n",
              "      <th>Above Knee dresses</th>\n",
              "      <th>Accessories</th>\n",
              "      <th>Adidas</th>\n",
              "      <th>American</th>\n",
              "      <th>Angel</th>\n",
              "      <th>Animal</th>\n",
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              "      <th>Army</th>\n",
              "      <th>Athletics</th>\n",
              "      <th>Baby</th>\n",
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              "      <th>Backs</th>\n",
              "      <th>Bags</th>\n",
              "      <th>Bali</th>\n",
              "      <th>Balloon</th>\n",
              "      <th>Bandage Dresses</th>\n",
              "      <th>...</th>\n",
              "      <th>womenshortpant</th>\n",
              "      <th>womenshortspant</th>\n",
              "      <th>womensleepwearset</th>\n",
              "      <th>womensleevelessdres</th>\n",
              "      <th>womensportoutfit</th>\n",
              "      <th>womensportset</th>\n",
              "      <th>womensporttracksuit</th>\n",
              "      <th>womenssexyshort</th>\n",
              "      <th>womensummerclothe</th>\n",
              "      <th>womensummeroutfit</th>\n",
              "      <th>womensummersexybacklessbikini</th>\n",
              "      <th>womensummershort</th>\n",
              "      <th>womensummerswimwear</th>\n",
              "      <th>womenswimbottom</th>\n",
              "      <th>womenswimsit</th>\n",
              "      <th>womenswimwearbikini</th>\n",
              "      <th>womentee</th>\n",
              "      <th>womenunderwearsuit</th>\n",
              "      <th>workout top</th>\n",
              "      <th>workoutampyoga</th>\n",
              "      <th>workoutshortsformen</th>\n",
              "      <th>woven</th>\n",
              "      <th>wovenbag</th>\n",
              "      <th>wrap dress</th>\n",
              "      <th>wrapjumpsuit</th>\n",
              "      <th>wrappedchest</th>\n",
              "      <th>yoga leggings</th>\n",
              "      <th>yoga pants</th>\n",
              "      <th>yoga top</th>\n",
              "      <th>yoga2pcsset</th>\n",
              "      <th>yogaromper</th>\n",
              "      <th>yogashort</th>\n",
              "      <th>yogasuit</th>\n",
              "      <th>yogasuitset</th>\n",
              "      <th>yogateeshirt</th>\n",
              "      <th>yogavest</th>\n",
              "      <th>young</th>\n",
              "      <th>zippers</th>\n",
              "      <th>zippersleevelessvest</th>\n",
              "      <th>漁師の帽子</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "<p>1063 rows × 2136 columns</p>\n",
              "</div>\n",
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            ],
            "text/plain": [
              "      #Sport Bra  #fashion #shorts  ...  zippersleevelessvest  漁師の帽子\n",
              "0              0                 0  ...                     0      0\n",
              "1              0                 0  ...                     0      0\n",
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              "4              0                 0  ...                     0      0\n",
              "...          ...               ...  ...                   ...    ...\n",
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              "1092           0                 0  ...                     0      0\n",
              "1093           0                 0  ...                     0      0\n",
              "\n",
              "[1063 rows x 2136 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I think there will be high correlation between tags"
      ],
      "metadata": {
        "id": "HRc8Q0TFI-WU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tags_df.sum().sort_values(ascending=False)[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "giqCM9nWKakY",
        "outputId": "1fba5db9-c137-40cc-b95f-96ce0c23b57d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Summer             893\n",
              "Women's Fashion    880\n",
              "Fashion            738\n",
              "Women              647\n",
              "Casual             608\n",
              "Plus Size          423\n",
              "sleeveless         390\n",
              "Dress              365\n",
              "Shorts             360\n",
              "Tops               335\n",
              "sexy               224\n",
              "Beach              211\n",
              "Sleeve             195\n",
              "short sleeves      193\n",
              "Shirt              186\n",
              "Print              184\n",
              "Tank               177\n",
              "Necks              169\n",
              "printed            159\n",
              "T Shirts           155\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get the first 20 tages\n",
        "top_20_tags = tags_df.sum().sort_values(ascending=False)[:20].index"
      ],
      "metadata": {
        "id": "EUiZV0jYH24w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sb\n",
        "corr = tags_df[top_20_tags].corr()\n",
        "plt.figure(figsize = (20,20))\n",
        "sb.heatmap(corr, cmap=\"Blues\", annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "M9feuQoRJOQU",
        "outputId": "0091a08f-0086-493d-c7ea-8c0747a93277"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fce505e0c90>"
            ]
          },
          "metadata": {},
          "execution_count": 214
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wrong assumption\n",
        "\n",
        "the is only high correlation between sleeve and short sleeves XD\n",
        "\n",
        "Let's chose only the top 10 columns which have the heigher correlation with the rating column"
      ],
      "metadata": {
        "id": "QbTwfXz8KzTu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tags_corr_rating = tags_df.corrwith(df_train['rating']).abs().sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "nvs83mo5H240"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pick only the top 10 columns with high correlation (heigher than 0.15)\n",
        "tags_high_corr_columns = tags_corr_rating[tags_corr_rating>0.15].index\n",
        "tags_high_corr_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rytaB2xHMXQc",
        "outputId": "1742806e-325b-401e-fe3c-a291b81aa22a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['linenjumpsuit', 'women long pants', 'Women Rompers', 'waistgirdle',\n",
              "       'slimming', 'wasitcincher', 'slimmingcorset', 'offshoulderjumpsuit',\n",
              "       'printjumpsuit', 'spaghettishoulderstrapdre', 'minidressforwomen',\n",
              "       'spaghettistrapdresse', 'womensfahsion', 'belt'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tags_high_corr_df = tags_df[tags_high_corr_columns]"
      ],
      "metadata": {
        "id": "5D4-rMtMH241"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### prepare data"
      ],
      "metadata": {
        "id": "qetp7p_jH241"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_4 = df_train.copy()"
      ],
      "metadata": {
        "id": "sTLCqR-yH242"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#drop nominal columns\n",
        "df_trail_4 = df_trail_4.drop(need_encoding_columns,axis = 1)\n",
        "#drop has_urgency_banner \n",
        "df_trail_4 = df_trail_4.drop(to_fill_columns,axis = 1)"
      ],
      "metadata": {
        "id": "skWdLQcDH242"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add tage encodes df\n",
        "df_trail_4 = pd.concat([df_trail_4, tags_high_corr_df], axis=1)"
      ],
      "metadata": {
        "id": "i1Bq2F7DH243"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_4.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee74f96d-a8e8-464c-a5c1-19419a15b774",
        "id": "dv7l2Ro4H243"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1063 entries, 0 to 1093\n",
            "Data columns (total 26 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   price                      1063 non-null   float64\n",
            " 1   retail_price               1063 non-null   int64  \n",
            " 2   units_sold                 1063 non-null   int64  \n",
            " 3   uses_ad_boosts             1063 non-null   int64  \n",
            " 4   rating                     1063 non-null   float64\n",
            " 5   rating_count               1063 non-null   int64  \n",
            " 6   badge_local_product        1063 non-null   int64  \n",
            " 7   badge_product_quality      1063 non-null   int64  \n",
            " 8   badge_fast_shipping        1063 non-null   int64  \n",
            " 9   shipping_option_price      1063 non-null   int64  \n",
            " 10  shipping_is_express        1063 non-null   int64  \n",
            " 11  merchant_rating            1063 non-null   float64\n",
            " 12  linenjumpsuit              1063 non-null   int64  \n",
            " 13  women long pants           1063 non-null   int64  \n",
            " 14  Women Rompers              1063 non-null   int64  \n",
            " 15  waistgirdle                1063 non-null   int64  \n",
            " 16  slimming                   1063 non-null   int64  \n",
            " 17  wasitcincher               1063 non-null   int64  \n",
            " 18  slimmingcorset             1063 non-null   int64  \n",
            " 19  offshoulderjumpsuit        1063 non-null   int64  \n",
            " 20  printjumpsuit              1063 non-null   int64  \n",
            " 21  spaghettishoulderstrapdre  1063 non-null   int64  \n",
            " 22  minidressforwomen          1063 non-null   int64  \n",
            " 23  spaghettistrapdresse       1063 non-null   int64  \n",
            " 24  womensfahsion              1063 non-null   int64  \n",
            " 25  belt                       1063 non-null   int64  \n",
            "dtypes: float64(3), int64(23)\n",
            "memory usage: 224.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_4.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10901c04-7e2a-4556-bcf2-e065d828c6ab",
        "id": "RpMsSygJH244"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_4_x, df_trail_4_y = x_y_split(df_trail_4)"
      ],
      "metadata": {
        "id": "efGOVEoxH245"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_4_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87e36152-dd96-46b5-e05d-e6c0c0374b1e",
        "id": "0L5zZJXZH245"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    774\n",
              "5.0    140\n",
              "3.0    135\n",
              "2.0     12\n",
              "1.0      2\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###GridSearch\n",
        "we will try three models with different parameters:\n",
        "1. Decision tree\n",
        "2. GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "pDRNKPzqH246"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This time we will reduce our searching arae using the preivious results (the best prams from the preivous trials)"
      ],
      "metadata": {
        "id": "JePp0EbsNKJp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling Data\n",
        "scaler = preprocessing.StandardScaler()\n",
        "df_trail_4_x_scaled = scaler.fit_transform(df_trail_4_x)"
      ],
      "metadata": {
        "id": "UKWsuAhXH246"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Decision Tree"
      ],
      "metadata": {
        "id": "mSG7cfvIH246"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DecisionTreeClassifier()\n",
        "dtreeparam_grid = {\n",
        "    'criterion': ['gini'],\n",
        "    'max_depth': np.arange(7,15),\n",
        "    # 'min_samples_leaf': np.arange(10),\n",
        "    'max_leaf_nodes': np.arange(45,65)\n",
        "}"
      ],
      "metadata": {
        "id": "nuz2QnmCH246"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [dtreeparam_grid]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_4_x_scaled,df_trail_4_y)\n",
        "\n",
        "grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_4_x_scaled,df_trail_4_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42be9a41-1a77-4749-f4cc-6eaf61ff820e",
        "id": "CHrx6obSH247"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 160 candidates, totalling 800 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce4d1c45d0>,\n",
              "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'criterion': ['gini'],\n",
              "                          'max_depth': array([ 7,  8,  9, 10, 11, 12, 13, 14]),\n",
              "                          'max_leaf_nodes': array([45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,\n",
              "       62, 63, 64])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DecisionTree_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "DecisionTree_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "DecisionTree_resutls[DecisionTree_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "d0c33fb9-ef7b-4f41-ff0e-c5074053f473",
        "id": "tLmqYpveH247"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_criterion</th>\n",
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              "      <th>param_max_leaf_nodes</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.005329</td>\n",
              "      <td>0.000179</td>\n",
              "      <td>0.002240</td>\n",
              "      <td>0.000157</td>\n",
              "      <td>gini</td>\n",
              "      <td>7</td>\n",
              "      <td>45</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 7, 'max_lea...</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.782718</td>\n",
              "      <td>0.012642</td>\n",
              "      <td>2</td>\n",
              "      <td>0.837647</td>\n",
              "      <td>0.842353</td>\n",
              "      <td>0.845882</td>\n",
              "      <td>0.835488</td>\n",
              "      <td>0.829612</td>\n",
              "      <td>0.838196</td>\n",
              "      <td>0.005616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.005165</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.002104</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>gini</td>\n",
              "      <td>7</td>\n",
              "      <td>64</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 7, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.782718</td>\n",
              "      <td>0.012288</td>\n",
              "      <td>2</td>\n",
              "      <td>0.838824</td>\n",
              "      <td>0.842353</td>\n",
              "      <td>0.845882</td>\n",
              "      <td>0.835488</td>\n",
              "      <td>0.829612</td>\n",
              "      <td>0.838432</td>\n",
              "      <td>0.005612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.006340</td>\n",
              "      <td>0.001784</td>\n",
              "      <td>0.002327</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>48</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.784591</td>\n",
              "      <td>0.012076</td>\n",
              "      <td>1</td>\n",
              "      <td>0.843529</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.856471</td>\n",
              "      <td>0.846063</td>\n",
              "      <td>0.837838</td>\n",
              "      <td>0.847604</td>\n",
              "      <td>0.006861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.005630</td>\n",
              "      <td>0.000298</td>\n",
              "      <td>0.002386</td>\n",
              "      <td>0.000183</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>50</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.793427</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.781765</td>\n",
              "      <td>0.012545</td>\n",
              "      <td>4</td>\n",
              "      <td>0.847059</td>\n",
              "      <td>0.856471</td>\n",
              "      <td>0.855294</td>\n",
              "      <td>0.847239</td>\n",
              "      <td>0.836663</td>\n",
              "      <td>0.848545</td>\n",
              "      <td>0.007120</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0        0.005329      0.000179  ...          0.838196         0.005616\n",
              "19       0.005165      0.000054  ...          0.838432         0.005612\n",
              "23       0.006340      0.001784  ...          0.847604         0.006861\n",
              "25       0.005630      0.000298  ...          0.848545         0.007120\n",
              "\n",
              "[4 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####GradientBoosting"
      ],
      "metadata": {
        "id": "3GGE_B3sH247"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GradientBoostingClassifier()\n",
        "GBC = {\n",
        "    'n_estimators': np.arange(80,120,10),\n",
        "    'learning_rate' : [0.2,0.1]\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "JWqhh3GjH247"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [GBC]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_4_x_scaled,df_trail_4_y)\n",
        "\n",
        "grid_search = GridSearchCV(GradientBoostingClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_4_x_scaled,df_trail_4_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d54b9da0-d119-4e44-f247-c29b30b5d6f5",
        "id": "iru1tULqH248"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce4d1c4ad0>,\n",
              "             estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'learning_rate': [0.2, 0.1],\n",
              "                          'n_estimators': array([ 80,  90, 100, 110])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBC_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "GBC_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "GBC_resutls[GBC_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "545cdd36-96b7-4ef8-e9d1-a3a41faf38dd",
        "id": "1BZgoZFQH248"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-41c585e1-7d64-499a-9fef-171d962b79f1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_learning_rate</th>\n",
              "      <th>param_n_estimators</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.261022</td>\n",
              "      <td>0.004901</td>\n",
              "      <td>0.004996</td>\n",
              "      <td>0.000095</td>\n",
              "      <td>0.1</td>\n",
              "      <td>80</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 80}</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.811321</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.788374</td>\n",
              "      <td>0.017643</td>\n",
              "      <td>3</td>\n",
              "      <td>0.908235</td>\n",
              "      <td>0.901176</td>\n",
              "      <td>0.897647</td>\n",
              "      <td>0.889542</td>\n",
              "      <td>0.897767</td>\n",
              "      <td>0.898874</td>\n",
              "      <td>0.006046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.409158</td>\n",
              "      <td>0.011405</td>\n",
              "      <td>0.005236</td>\n",
              "      <td>0.000223</td>\n",
              "      <td>0.1</td>\n",
              "      <td>90</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 90}</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.791186</td>\n",
              "      <td>0.014024</td>\n",
              "      <td>2</td>\n",
              "      <td>0.915294</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>0.904706</td>\n",
              "      <td>0.897767</td>\n",
              "      <td>0.902468</td>\n",
              "      <td>0.905929</td>\n",
              "      <td>0.005999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.578294</td>\n",
              "      <td>0.013508</td>\n",
              "      <td>0.005612</td>\n",
              "      <td>0.000313</td>\n",
              "      <td>0.1</td>\n",
              "      <td>100</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.811321</td>\n",
              "      <td>0.787435</td>\n",
              "      <td>0.017970</td>\n",
              "      <td>4</td>\n",
              "      <td>0.922353</td>\n",
              "      <td>0.916471</td>\n",
              "      <td>0.915294</td>\n",
              "      <td>0.903643</td>\n",
              "      <td>0.914219</td>\n",
              "      <td>0.914396</td>\n",
              "      <td>0.006068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.706667</td>\n",
              "      <td>0.057658</td>\n",
              "      <td>0.005397</td>\n",
              "      <td>0.000868</td>\n",
              "      <td>0.1</td>\n",
              "      <td>110</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 110}</td>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.811321</td>\n",
              "      <td>0.794007</td>\n",
              "      <td>0.012655</td>\n",
              "      <td>1</td>\n",
              "      <td>0.923529</td>\n",
              "      <td>0.927059</td>\n",
              "      <td>0.922353</td>\n",
              "      <td>0.911868</td>\n",
              "      <td>0.924794</td>\n",
              "      <td>0.921921</td>\n",
              "      <td>0.005263</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41c585e1-7d64-499a-9fef-171d962b79f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-41c585e1-7d64-499a-9fef-171d962b79f1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-41c585e1-7d64-499a-9fef-171d962b79f1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "4       1.261022      0.004901  ...          0.898874         0.006046\n",
              "5       1.409158      0.011405  ...          0.905929         0.005999\n",
              "6       1.578294      0.013508  ...          0.914396         0.006068\n",
              "7       1.706667      0.057658  ...          0.921921         0.005263\n",
              "\n",
              "[4 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Inspect results"
      ],
      "metadata": {
        "id": "gKvRUjLCH248"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('DT best f1-score:{0} , best prams:{1}'.format(DecisionTree_best[0],DecisionTree_best[1]))\n",
        "print('GBC best f1-score:{0} , best prams:{1}'.format(GBC_best[0],GBC_best[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b544202a-145f-4263-f7e4-2a02675b06f2",
        "id": "gxtlyHWbH249"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DT best f1-score:0.7845911949685536 , best prams:{'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 48}\n",
            "GBC best f1-score:0.7940074408716449 , best prams:{'learning_rate': 0.1, 'n_estimators': 110}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The f1-score reduced :(\n",
        "\n",
        "  but this is expexted as we are dropped some columns which means we lost some information"
      ],
      "metadata": {
        "id": "0B3lvYdSOegc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "q_MqTkXuPbuZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trial 5 (add color column)\n",
        "* prepare color column \n",
        "* prepare data (drop nominal columns and has_urgency_banner column then add color column)\n",
        "* use gridsearch (DT and GB only)\n",
        "* inspect results \n"
      ],
      "metadata": {
        "id": "Agjz3x8TPc6Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###prepare tage column"
      ],
      "metadata": {
        "id": "8vMWhdgCPc6R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "color_columns = df_train['product_color'].copy()"
      ],
      "metadata": {
        "id": "Y4ou0eRMPc6R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "color_columns.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37611867-edc5-49a7-9507-488ececac25d",
        "id": "ldzQofCsPc6R"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we want to keep 85% of color and change the remaining 15% to others\n",
        "#so we use the commulative sum to sum the persentages of colors in descending order\n",
        "cumsum_color = (color_columns.value_counts()/color_columns.count()).cumsum()\n",
        "cumsum_color"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TVO3CmK1QIw9",
        "outputId": "4514c8c6-7144-43bf-c209-abde3b9411e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "black               0.204436\n",
              "white               0.365477\n",
              "blue                0.437801\n",
              "pink                0.503375\n",
              "yellow              0.566056\n",
              "                      ...   \n",
              "lightgrey           0.996143\n",
              "star                0.997107\n",
              "lightyellow         0.998071\n",
              "gold                0.999036\n",
              "navyblue & white    1.000000\n",
              "Name: product_color, Length: 85, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#then apply our threshold\n",
        "chosen_colors = cumsum_color[cumsum_color < 0.85].index\n",
        "chosen_colors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WF5pWzXdRqCS",
        "outputId": "fad9c616-2fdd-4b30-b2d8-e87a2481cd3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['black', 'white', 'blue', 'pink', 'yellow', 'red', 'green', 'grey',\n",
              "       'purple', 'armygreen', 'winered', 'navyblue', 'multicolor', 'orange'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#change the value of other colors to other\n",
        "color_columns[~color_columns.isin(chosen_colors)] = 'other'"
      ],
      "metadata": {
        "id": "u0L8B82tR_sP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "color_columns.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hteDTsDaTpT9",
        "outputId": "9bd42d2f-8c24-493c-a976-c14717d9d381"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "black         212\n",
              "other         184\n",
              "white         167\n",
              "blue           75\n",
              "pink           68\n",
              "yellow         65\n",
              "red            61\n",
              "green          61\n",
              "grey           48\n",
              "purple         36\n",
              "armygreen      21\n",
              "winered        19\n",
              "navyblue       18\n",
              "multicolor     14\n",
              "orange         14\n",
              "Name: product_color, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "color_columns.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyAfIZk9Ttmn",
        "outputId": "84369c45-e354-49f3-cf18-d884d00bc96f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colors_df = color_columns.str.get_dummies(',')\n",
        "colors_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "3809e302-4713-4156-fd7e-5c4932843c0e",
        "id": "kUX7ZprkPc6S"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</table>\n",
              "<p>1063 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e3efdb8-21d0-429d-aacc-e969b012913f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6e3efdb8-21d0-429d-aacc-e969b012913f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6e3efdb8-21d0-429d-aacc-e969b012913f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      armygreen  black  blue  green  grey  ...  purple  red  white  winered  yellow\n",
              "0             0      0     0      0     0  ...       0    0      0        0       1\n",
              "1             0      1     0      0     0  ...       0    0      0        0       0\n",
              "2             0      0     0      0     0  ...       0    0      1        0       0\n",
              "3             0      0     0      0     0  ...       0    0      0        0       0\n",
              "4             0      0     0      0     0  ...       0    0      0        0       0\n",
              "...         ...    ...   ...    ...   ...  ...     ...  ...    ...      ...     ...\n",
              "1089          0      0     0      0     0  ...       0    1      0        0       0\n",
              "1090          0      0     0      0     0  ...       0    0      0        0       0\n",
              "1091          0      0     0      0     0  ...       1    0      0        0       0\n",
              "1092          0      1     0      0     0  ...       0    0      0        0       0\n",
              "1093          0      0     0      0     0  ...       0    0      0        0       1\n",
              "\n",
              "[1063 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### prepare data"
      ],
      "metadata": {
        "id": "Ha3ltmGUPc6T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_5 = df_train.copy()"
      ],
      "metadata": {
        "id": "nhyGr9nmPc6U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#drop nominal columns\n",
        "df_trail_5 = df_trail_5.drop(need_encoding_columns,axis = 1)\n",
        "#drop has_urgency_banner \n",
        "df_trail_5 = df_trail_5.drop(to_fill_columns,axis = 1)"
      ],
      "metadata": {
        "id": "IeCU2h1TPc6U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add tage encodes df\n",
        "df_trail_5 = pd.concat([df_trail_5, colors_df], axis=1)"
      ],
      "metadata": {
        "id": "bA9QSt7cPc6U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_5.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33b7c45f-70e6-467f-edbf-e7cb9a3f3765",
        "id": "J-6sajcCPc6U"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1063 entries, 0 to 1093\n",
            "Data columns (total 27 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   price                  1063 non-null   float64\n",
            " 1   retail_price           1063 non-null   int64  \n",
            " 2   units_sold             1063 non-null   int64  \n",
            " 3   uses_ad_boosts         1063 non-null   int64  \n",
            " 4   rating                 1063 non-null   float64\n",
            " 5   rating_count           1063 non-null   int64  \n",
            " 6   badge_local_product    1063 non-null   int64  \n",
            " 7   badge_product_quality  1063 non-null   int64  \n",
            " 8   badge_fast_shipping    1063 non-null   int64  \n",
            " 9   shipping_option_price  1063 non-null   int64  \n",
            " 10  shipping_is_express    1063 non-null   int64  \n",
            " 11  merchant_rating        1063 non-null   float64\n",
            " 12  armygreen              1063 non-null   int64  \n",
            " 13  black                  1063 non-null   int64  \n",
            " 14  blue                   1063 non-null   int64  \n",
            " 15  green                  1063 non-null   int64  \n",
            " 16  grey                   1063 non-null   int64  \n",
            " 17  multicolor             1063 non-null   int64  \n",
            " 18  navyblue               1063 non-null   int64  \n",
            " 19  orange                 1063 non-null   int64  \n",
            " 20  other                  1063 non-null   int64  \n",
            " 21  pink                   1063 non-null   int64  \n",
            " 22  purple                 1063 non-null   int64  \n",
            " 23  red                    1063 non-null   int64  \n",
            " 24  white                  1063 non-null   int64  \n",
            " 25  winered                1063 non-null   int64  \n",
            " 26  yellow                 1063 non-null   int64  \n",
            "dtypes: float64(3), int64(24)\n",
            "memory usage: 232.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_5.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f45f8783-feba-44fe-8e92-6ffa17253be2",
        "id": "kqA5OP_rPc6V"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_5_x, df_trail_5_y = x_y_split(df_trail_5)"
      ],
      "metadata": {
        "id": "AsQaIl7dPc6V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_5_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79890cbd-9e5a-49bb-cdac-b95d921c8850",
        "id": "dgF5Ze2IPc6V"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    774\n",
              "5.0    140\n",
              "3.0    135\n",
              "2.0     12\n",
              "1.0      2\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###GridSearch\n",
        "we will try three models with different parameters:\n",
        "1. Decision tree\n",
        "2. GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "FSVYjxdmPc6W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling Data\n",
        "scaler = preprocessing.StandardScaler()\n",
        "df_trail_5_x_scaled = scaler.fit_transform(df_trail_5_x)"
      ],
      "metadata": {
        "id": "8qp_ErsHPc6X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Decision Tree"
      ],
      "metadata": {
        "id": "CnVZ8_yOPc6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DecisionTreeClassifier()\n",
        "dtreeparam_grid = {\n",
        "    'criterion': ['gini'],\n",
        "    'max_depth': np.arange(7,15),\n",
        "    # 'min_samples_leaf': np.arange(10),\n",
        "    'max_leaf_nodes': np.arange(45,65)\n",
        "}"
      ],
      "metadata": {
        "id": "tD2RBbrFPc6X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [dtreeparam_grid]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_5_x_scaled,df_trail_5_y)\n",
        "\n",
        "grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_5_x_scaled,df_trail_5_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d981dc4-8fa1-445d-eaec-fa4d9aa46961",
        "id": "8i9rIlMGPc6Y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 160 candidates, totalling 800 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce4d1be3d0>,\n",
              "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'criterion': ['gini'],\n",
              "                          'max_depth': array([ 7,  8,  9, 10, 11, 12, 13, 14]),\n",
              "                          'max_leaf_nodes': array([45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,\n",
              "       62, 63, 64])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DecisionTree_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "DecisionTree_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "DecisionTree_resutls[DecisionTree_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "0ce7e776-cf07-4e0b-e660-c469336f4ba9",
        "id": "bjSLgwx9Pc6Y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c06341eb-925c-44c4-b36a-b475eda85e6b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_criterion</th>\n",
              "      <th>param_max_depth</th>\n",
              "      <th>param_max_leaf_nodes</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.006262</td>\n",
              "      <td>0.001645</td>\n",
              "      <td>0.002160</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>gini</td>\n",
              "      <td>7</td>\n",
              "      <td>45</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 7, 'max_lea...</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.778302</td>\n",
              "      <td>0.767663</td>\n",
              "      <td>0.014040</td>\n",
              "      <td>4</td>\n",
              "      <td>0.843529</td>\n",
              "      <td>0.845882</td>\n",
              "      <td>0.848235</td>\n",
              "      <td>0.836663</td>\n",
              "      <td>0.826087</td>\n",
              "      <td>0.840079</td>\n",
              "      <td>0.007994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.005966</td>\n",
              "      <td>0.000558</td>\n",
              "      <td>0.002263</td>\n",
              "      <td>0.000092</td>\n",
              "      <td>gini</td>\n",
              "      <td>7</td>\n",
              "      <td>46</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 7, 'max_lea...</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.778302</td>\n",
              "      <td>0.767663</td>\n",
              "      <td>0.015531</td>\n",
              "      <td>4</td>\n",
              "      <td>0.843529</td>\n",
              "      <td>0.845882</td>\n",
              "      <td>0.849412</td>\n",
              "      <td>0.836663</td>\n",
              "      <td>0.826087</td>\n",
              "      <td>0.840315</td>\n",
              "      <td>0.008244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.007461</td>\n",
              "      <td>0.003657</td>\n",
              "      <td>0.003265</td>\n",
              "      <td>0.002186</td>\n",
              "      <td>gini</td>\n",
              "      <td>7</td>\n",
              "      <td>56</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 7, 'max_lea...</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.778302</td>\n",
              "      <td>0.767663</td>\n",
              "      <td>0.015531</td>\n",
              "      <td>4</td>\n",
              "      <td>0.843529</td>\n",
              "      <td>0.845882</td>\n",
              "      <td>0.848235</td>\n",
              "      <td>0.837838</td>\n",
              "      <td>0.826087</td>\n",
              "      <td>0.840314</td>\n",
              "      <td>0.007907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.005382</td>\n",
              "      <td>0.000057</td>\n",
              "      <td>0.002152</td>\n",
              "      <td>0.000251</td>\n",
              "      <td>gini</td>\n",
              "      <td>7</td>\n",
              "      <td>60</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 7, 'max_lea...</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.746479</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.767672</td>\n",
              "      <td>0.017805</td>\n",
              "      <td>2</td>\n",
              "      <td>0.843529</td>\n",
              "      <td>0.845882</td>\n",
              "      <td>0.848235</td>\n",
              "      <td>0.837838</td>\n",
              "      <td>0.826087</td>\n",
              "      <td>0.840314</td>\n",
              "      <td>0.007907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0.005773</td>\n",
              "      <td>0.000091</td>\n",
              "      <td>0.002044</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>gini</td>\n",
              "      <td>9</td>\n",
              "      <td>48</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 9, 'max_lea...</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.778302</td>\n",
              "      <td>0.773585</td>\n",
              "      <td>0.768593</td>\n",
              "      <td>0.010979</td>\n",
              "      <td>1</td>\n",
              "      <td>0.849412</td>\n",
              "      <td>0.857647</td>\n",
              "      <td>0.856471</td>\n",
              "      <td>0.854289</td>\n",
              "      <td>0.853114</td>\n",
              "      <td>0.854186</td>\n",
              "      <td>0.002869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.006100</td>\n",
              "      <td>0.000402</td>\n",
              "      <td>0.001956</td>\n",
              "      <td>0.000129</td>\n",
              "      <td>gini</td>\n",
              "      <td>9</td>\n",
              "      <td>63</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 9, 'max_lea...</td>\n",
              "      <td>0.737089</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.767672</td>\n",
              "      <td>0.019909</td>\n",
              "      <td>2</td>\n",
              "      <td>0.863529</td>\n",
              "      <td>0.869412</td>\n",
              "      <td>0.868235</td>\n",
              "      <td>0.867215</td>\n",
              "      <td>0.864865</td>\n",
              "      <td>0.866651</td>\n",
              "      <td>0.002163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c06341eb-925c-44c4-b36a-b475eda85e6b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c06341eb-925c-44c4-b36a-b475eda85e6b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c06341eb-925c-44c4-b36a-b475eda85e6b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0        0.006262      0.001645  ...          0.840079         0.007994\n",
              "1        0.005966      0.000558  ...          0.840315         0.008244\n",
              "11       0.007461      0.003657  ...          0.840314         0.007907\n",
              "15       0.005382      0.000057  ...          0.840314         0.007907\n",
              "43       0.005773      0.000091  ...          0.854186         0.002869\n",
              "58       0.006100      0.000402  ...          0.866651         0.002163\n",
              "\n",
              "[6 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####GradientBoosting"
      ],
      "metadata": {
        "id": "KsLmuIs0Pc6Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GradientBoostingClassifier()\n",
        "GBC = {\n",
        "    'n_estimators': np.arange(80,120,10),\n",
        "    'learning_rate' : [0.2,0.1]\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "1ad5ef8CPc6Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [GBC]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_5_x_scaled,df_trail_5_y)\n",
        "\n",
        "grid_search = GridSearchCV(GradientBoostingClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_5_x_scaled,df_trail_5_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cda29719-1411-4269-a9a4-5b102ec85c10",
        "id": "8S1H8nZGPc6Z"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce4d1c4d50>,\n",
              "             estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'learning_rate': [0.2, 0.1],\n",
              "                          'n_estimators': array([ 80,  90, 100, 110])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBC_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "GBC_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "GBC_resutls[GBC_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "60252e9c-bbe9-4a99-8da2-ccdc9e854702",
        "id": "XgBRNs9CPc6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_learning_rate</th>\n",
              "      <th>param_n_estimators</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.293018</td>\n",
              "      <td>0.021161</td>\n",
              "      <td>0.005462</td>\n",
              "      <td>0.000784</td>\n",
              "      <td>0.2</td>\n",
              "      <td>80</td>\n",
              "      <td>{'learning_rate': 0.2, 'n_estimators': 80}</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.778971</td>\n",
              "      <td>0.020368</td>\n",
              "      <td>1</td>\n",
              "      <td>0.967059</td>\n",
              "      <td>0.948235</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.968273</td>\n",
              "      <td>0.964747</td>\n",
              "      <td>0.961663</td>\n",
              "      <td>0.007285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.595759</td>\n",
              "      <td>0.013288</td>\n",
              "      <td>0.005666</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>0.2</td>\n",
              "      <td>100</td>\n",
              "      <td>{'learning_rate': 0.2, 'n_estimators': 100}</td>\n",
              "      <td>0.732394</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.801887</td>\n",
              "      <td>0.801887</td>\n",
              "      <td>0.776154</td>\n",
              "      <td>0.025954</td>\n",
              "      <td>3</td>\n",
              "      <td>0.982353</td>\n",
              "      <td>0.975294</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.981199</td>\n",
              "      <td>0.972973</td>\n",
              "      <td>0.978364</td>\n",
              "      <td>0.003609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.271831</td>\n",
              "      <td>0.004753</td>\n",
              "      <td>0.006361</td>\n",
              "      <td>0.002607</td>\n",
              "      <td>0.1</td>\n",
              "      <td>80</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 80}</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.801887</td>\n",
              "      <td>0.778036</td>\n",
              "      <td>0.022845</td>\n",
              "      <td>2</td>\n",
              "      <td>0.905882</td>\n",
              "      <td>0.898824</td>\n",
              "      <td>0.898824</td>\n",
              "      <td>0.898942</td>\n",
              "      <td>0.910693</td>\n",
              "      <td>0.902633</td>\n",
              "      <td>0.004862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.620717</td>\n",
              "      <td>0.008697</td>\n",
              "      <td>0.005556</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.1</td>\n",
              "      <td>100</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
              "      <td>0.751174</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.776149</td>\n",
              "      <td>0.020486</td>\n",
              "      <td>4</td>\n",
              "      <td>0.928235</td>\n",
              "      <td>0.916471</td>\n",
              "      <td>0.921176</td>\n",
              "      <td>0.916569</td>\n",
              "      <td>0.924794</td>\n",
              "      <td>0.921449</td>\n",
              "      <td>0.004603</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d7d3d99-29d5-4e1a-a9d0-29a9da69a938')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5d7d3d99-29d5-4e1a-a9d0-29a9da69a938 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5d7d3d99-29d5-4e1a-a9d0-29a9da69a938');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0       1.293018      0.021161  ...          0.961663         0.007285\n",
              "2       1.595759      0.013288  ...          0.978364         0.003609\n",
              "4       1.271831      0.004753  ...          0.902633         0.004862\n",
              "6       1.620717      0.008697  ...          0.921449         0.004603\n",
              "\n",
              "[4 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Inspect results"
      ],
      "metadata": {
        "id": "kNCBqb5sPc6a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('DT best f1-score:{0} , best prams:{1}'.format(DecisionTree_best[0],DecisionTree_best[1]))\n",
        "print('GBC best f1-score:{0} , best prams:{1}'.format(GBC_best[0],GBC_best[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70ad5782-62ad-4aa1-b477-02cb24ae3f56",
        "id": "QzP1FFB9Pc6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DT best f1-score:0.7685933209318806 , best prams:{'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 48}\n",
            "GBC best f1-score:0.7789706794224467 , best prams:{'learning_rate': 0.2, 'n_estimators': 80}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "results are nearly the same as the first trial (with no nominal features)"
      ],
      "metadata": {
        "id": "8FvALabNPc6a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ppaMQ2X0VWlT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trial 6 (add orgin country column)\n",
        "* prepare orgin country column \n",
        "* prepare data (drop nominal columns and has_urgency_banner column then add orgin country column)\n",
        "* use gridsearch (DT and GB only)\n",
        "* inspect results"
      ],
      "metadata": {
        "id": "xcpGi8EeVYlG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###prepare orgin country column"
      ],
      "metadata": {
        "id": "7kVeQ7H1VYlH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['origin_country'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3G2-W7vnVhsE",
        "outputId": "468ad4b7-0dd9-4984-c102-0eceb69447f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CN    1023\n",
              "US      22\n",
              "VE       3\n",
              "SG       2\n",
              "GB       1\n",
              "Name: origin_country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "origin_country_columns = df_train['origin_country'].copy()"
      ],
      "metadata": {
        "id": "NQwzsYneVYlI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "origin_country_columns.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55a91b7c-4edf-438a-ef37-38bed280ddaf",
        "id": "1c6Td2M1VYlJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fill na with 'other'\n",
        "origin_country_columns.fillna('other',inplace = True)"
      ],
      "metadata": {
        "id": "3ivmTPxIVxMx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "origin_country_df = origin_country_columns.str.get_dummies(',')\n",
        "origin_country_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "5a36da36-52ce-40ab-9349-0ee2210e30da",
        "id": "C5jMEMphVYlS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-71597912-dc06-4213-a766-d4dbfd95873f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "      <th></th>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-71597912-dc06-4213-a766-d4dbfd95873f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-71597912-dc06-4213-a766-d4dbfd95873f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      CN  GB  SG  US  VE  other\n",
              "0      1   0   0   0   0      0\n",
              "1      1   0   0   0   0      0\n",
              "2      1   0   0   0   0      0\n",
              "3      1   0   0   0   0      0\n",
              "4      1   0   0   0   0      0\n",
              "...   ..  ..  ..  ..  ..    ...\n",
              "1089   1   0   0   0   0      0\n",
              "1090   1   0   0   0   0      0\n",
              "1091   1   0   0   0   0      0\n",
              "1092   1   0   0   0   0      0\n",
              "1093   1   0   0   0   0      0\n",
              "\n",
              "[1063 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### prepare data"
      ],
      "metadata": {
        "id": "M39IXEkdVYlT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_6 = df_train.copy()"
      ],
      "metadata": {
        "id": "coAp9ZTGVYlU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#drop nominal columns\n",
        "df_trail_6 = df_trail_6.drop(need_encoding_columns,axis = 1)\n",
        "#drop has_urgency_banner \n",
        "df_trail_6 = df_trail_6.drop(to_fill_columns,axis = 1)"
      ],
      "metadata": {
        "id": "ie0AtaLJVYlU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add tage encodes df\n",
        "df_trail_6 = pd.concat([df_trail_6, origin_country_df], axis=1)"
      ],
      "metadata": {
        "id": "_UGxDtUMVYlV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_6.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61a9b31f-15a2-4fca-aec0-3026635b275e",
        "id": "xQeVKKblVYlV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1063 entries, 0 to 1093\n",
            "Data columns (total 18 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   price                  1063 non-null   float64\n",
            " 1   retail_price           1063 non-null   int64  \n",
            " 2   units_sold             1063 non-null   int64  \n",
            " 3   uses_ad_boosts         1063 non-null   int64  \n",
            " 4   rating                 1063 non-null   float64\n",
            " 5   rating_count           1063 non-null   int64  \n",
            " 6   badge_local_product    1063 non-null   int64  \n",
            " 7   badge_product_quality  1063 non-null   int64  \n",
            " 8   badge_fast_shipping    1063 non-null   int64  \n",
            " 9   shipping_option_price  1063 non-null   int64  \n",
            " 10  shipping_is_express    1063 non-null   int64  \n",
            " 11  merchant_rating        1063 non-null   float64\n",
            " 12  CN                     1063 non-null   int64  \n",
            " 13  GB                     1063 non-null   int64  \n",
            " 14  SG                     1063 non-null   int64  \n",
            " 15  US                     1063 non-null   int64  \n",
            " 16  VE                     1063 non-null   int64  \n",
            " 17  other                  1063 non-null   int64  \n",
            "dtypes: float64(3), int64(15)\n",
            "memory usage: 157.8 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_6.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cb933cd-2e72-4105-ddd4-ab634af3f71d",
        "id": "JCFEov_KVYlW"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_6_x, df_trail_6_y = x_y_split(df_trail_6)"
      ],
      "metadata": {
        "id": "4YnamKdJVYlW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_trail_6_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ae0c53b-3861-4d97-dc9b-d8284bbf0c84",
        "id": "K6B1pwzbVYlX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    774\n",
              "5.0    140\n",
              "3.0    135\n",
              "2.0     12\n",
              "1.0      2\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###GridSearch\n",
        "we will try three models with different parameters:\n",
        "1. Decision tree\n",
        "2. GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "AVlDORUoVYlX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling Data\n",
        "scaler = preprocessing.StandardScaler()\n",
        "df_trail_6_x_scaled = scaler.fit_transform(df_trail_6_x)"
      ],
      "metadata": {
        "id": "e2LOCHsJVYlY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Decision Tree"
      ],
      "metadata": {
        "id": "iKYPC_OJVYlY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DecisionTreeClassifier()\n",
        "dtreeparam_grid = {\n",
        "    'criterion': ['gini'],\n",
        "    'max_depth': np.arange(7,15),\n",
        "    # 'min_samples_leaf': np.arange(10),\n",
        "    'max_leaf_nodes': np.arange(45,65)\n",
        "}"
      ],
      "metadata": {
        "id": "tJ1FAPw1VYlZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [dtreeparam_grid]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_6_x_scaled,df_trail_6_y)\n",
        "\n",
        "grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_6_x_scaled,df_trail_6_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24153894-694f-4d10-d299-809a09720539",
        "id": "1ZowsYFyVYla"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 160 candidates, totalling 800 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce4d1c49d0>,\n",
              "             estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'criterion': ['gini'],\n",
              "                          'max_depth': array([ 7,  8,  9, 10, 11, 12, 13, 14]),\n",
              "                          'max_leaf_nodes': array([45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,\n",
              "       62, 63, 64])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DecisionTree_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "DecisionTree_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "DecisionTree_resutls[DecisionTree_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "9fa7e370-6860-4c02-bd70-bd045a28c1bb",
        "id": "R7SVg1q1VYla"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_criterion</th>\n",
              "      <th>param_max_depth</th>\n",
              "      <th>param_max_leaf_nodes</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.005110</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.002253</td>\n",
              "      <td>0.000652</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>48</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.779888</td>\n",
              "      <td>0.012759</td>\n",
              "      <td>2</td>\n",
              "      <td>0.840000</td>\n",
              "      <td>0.850588</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.840188</td>\n",
              "      <td>0.847239</td>\n",
              "      <td>0.846426</td>\n",
              "      <td>0.005610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.005175</td>\n",
              "      <td>0.000199</td>\n",
              "      <td>0.002146</td>\n",
              "      <td>0.000268</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>50</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.779883</td>\n",
              "      <td>0.008744</td>\n",
              "      <td>3</td>\n",
              "      <td>0.841176</td>\n",
              "      <td>0.851765</td>\n",
              "      <td>0.855294</td>\n",
              "      <td>0.841363</td>\n",
              "      <td>0.848414</td>\n",
              "      <td>0.847602</td>\n",
              "      <td>0.005610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.006856</td>\n",
              "      <td>0.003486</td>\n",
              "      <td>0.002069</td>\n",
              "      <td>0.000153</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>52</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.779888</td>\n",
              "      <td>0.010483</td>\n",
              "      <td>1</td>\n",
              "      <td>0.844706</td>\n",
              "      <td>0.852941</td>\n",
              "      <td>0.858824</td>\n",
              "      <td>0.842538</td>\n",
              "      <td>0.848414</td>\n",
              "      <td>0.849484</td>\n",
              "      <td>0.005855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.005450</td>\n",
              "      <td>0.000363</td>\n",
              "      <td>0.002084</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>gini</td>\n",
              "      <td>8</td>\n",
              "      <td>63</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 8, 'max_lea...</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.755869</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.778010</td>\n",
              "      <td>0.013377</td>\n",
              "      <td>4</td>\n",
              "      <td>0.850588</td>\n",
              "      <td>0.854118</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.847239</td>\n",
              "      <td>0.850764</td>\n",
              "      <td>0.852542</td>\n",
              "      <td>0.004318</td>\n",
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              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "23       0.005110      0.000104  ...          0.846426         0.005610\n",
              "25       0.005175      0.000199  ...          0.847602         0.005610\n",
              "27       0.006856      0.003486  ...          0.849484         0.005855\n",
              "38       0.005450      0.000363  ...          0.852542         0.004318\n",
              "\n",
              "[4 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####GradientBoosting"
      ],
      "metadata": {
        "id": "FadA_SPiVYlb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GradientBoostingClassifier()\n",
        "GBC = {\n",
        "    'n_estimators': np.arange(80,120,10),\n",
        "    'learning_rate' : [0.2,0.1]\n",
        "\n",
        "}"
      ],
      "metadata": {
        "id": "BOla0YMcVYlc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_list = [GBC]\n",
        "\n",
        "#prepare StratifiedKFold and use shuffle\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1).split(df_trail_6_x_scaled,df_trail_6_y)\n",
        "\n",
        "grid_search = GridSearchCV(GradientBoostingClassifier(), param_grid = param_list ,\n",
        "                           refit = True, cv= cv,return_train_score=True,\n",
        "                           scoring = 'f1_micro',n_jobs=-1,verbose = 3)\n",
        "grid_search.fit(df_trail_6_x_scaled,df_trail_6_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "430600bf-84ae-409c-94ce-5be57a330d63",
        "id": "lrwiIvu4VYlc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7fce4d1be550>,\n",
              "             estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid=[{'learning_rate': [0.2, 0.1],\n",
              "                          'n_estimators': array([ 80,  90, 100, 110])}],\n",
              "             return_train_score=True, scoring='f1_micro', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GBC_resutls = pd.DataFrame(grid_search.cv_results_)\n",
        "GBC_best = [grid_search.best_score_ , grid_search.best_params_]\n",
        "GBC_resutls[GBC_resutls['rank_test_score'] < 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "deba8669-8236-4da7-eff6-698efa60ae8f",
        "id": "1vTi4b2WVYld"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_learning_rate</th>\n",
              "      <th>param_n_estimators</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.225712</td>\n",
              "      <td>0.023243</td>\n",
              "      <td>0.005140</td>\n",
              "      <td>0.000177</td>\n",
              "      <td>0.2</td>\n",
              "      <td>80</td>\n",
              "      <td>{'learning_rate': 0.2, 'n_estimators': 80}</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.784038</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.801887</td>\n",
              "      <td>0.783652</td>\n",
              "      <td>0.011118</td>\n",
              "      <td>1</td>\n",
              "      <td>0.965882</td>\n",
              "      <td>0.955294</td>\n",
              "      <td>0.957647</td>\n",
              "      <td>0.951821</td>\n",
              "      <td>0.955347</td>\n",
              "      <td>0.957198</td>\n",
              "      <td>0.004724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.222987</td>\n",
              "      <td>0.013211</td>\n",
              "      <td>0.005141</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>0.1</td>\n",
              "      <td>80</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 80}</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.774648</td>\n",
              "      <td>0.765258</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.778962</td>\n",
              "      <td>0.016649</td>\n",
              "      <td>3</td>\n",
              "      <td>0.904706</td>\n",
              "      <td>0.904706</td>\n",
              "      <td>0.896471</td>\n",
              "      <td>0.886016</td>\n",
              "      <td>0.902468</td>\n",
              "      <td>0.898873</td>\n",
              "      <td>0.007100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.368124</td>\n",
              "      <td>0.008103</td>\n",
              "      <td>0.005523</td>\n",
              "      <td>0.000194</td>\n",
              "      <td>0.1</td>\n",
              "      <td>90</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 90}</td>\n",
              "      <td>0.760563</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.787736</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.778962</td>\n",
              "      <td>0.016382</td>\n",
              "      <td>3</td>\n",
              "      <td>0.918824</td>\n",
              "      <td>0.912941</td>\n",
              "      <td>0.904706</td>\n",
              "      <td>0.895417</td>\n",
              "      <td>0.910693</td>\n",
              "      <td>0.908516</td>\n",
              "      <td>0.007958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.540686</td>\n",
              "      <td>0.014908</td>\n",
              "      <td>0.005573</td>\n",
              "      <td>0.000161</td>\n",
              "      <td>0.1</td>\n",
              "      <td>100</td>\n",
              "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.769953</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.783019</td>\n",
              "      <td>0.811321</td>\n",
              "      <td>0.782718</td>\n",
              "      <td>0.015202</td>\n",
              "      <td>2</td>\n",
              "      <td>0.922353</td>\n",
              "      <td>0.915294</td>\n",
              "      <td>0.916471</td>\n",
              "      <td>0.898942</td>\n",
              "      <td>0.917744</td>\n",
              "      <td>0.914161</td>\n",
              "      <td>0.007977</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe8bb3bf-8132-4be6-9571-033be88db002')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fe8bb3bf-8132-4be6-9571-033be88db002 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fe8bb3bf-8132-4be6-9571-033be88db002');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0       1.225712      0.023243  ...          0.957198         0.004724\n",
              "4       1.222987      0.013211  ...          0.898873         0.007100\n",
              "5       1.368124      0.008103  ...          0.908516         0.007958\n",
              "6       1.540686      0.014908  ...          0.914161         0.007977\n",
              "\n",
              "[4 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Inspect results"
      ],
      "metadata": {
        "id": "eBb-vsl5VYle"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('DT best f1-score:{0} , best prams:{1}'.format(DecisionTree_best[0],DecisionTree_best[1]))\n",
        "print('GBC best f1-score:{0} , best prams:{1}'.format(GBC_best[0],GBC_best[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c53ee3bb-22ab-4271-9b46-ed72aacd375e",
        "id": "yVp_g1o6VYlf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DT best f1-score:0.7798875011072727 , best prams:{'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 52}\n",
            "GBC best f1-score:0.7836522278324032 , best prams:{'learning_rate': 0.2, 'n_estimators': 80}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "results are nearly the same as the first trial (with no nominal features)"
      ],
      "metadata": {
        "id": "mR9G6KlPVYlf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#Testing\n",
        "Now we will use our best trial to predict the rating of test data.\n",
        "\n",
        "Trial 3 is the best one. So we wil:\n",
        "1. Train the model with highest f1-score\n",
        "2. Prepare teat data as we did for training data in trail 3\n",
        "3. Use the model for prediction"
      ],
      "metadata": {
        "id": "oyr2VLFiRpDO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##train the model"
      ],
      "metadata": {
        "id": "vjQDl1PDs527"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling Data\n",
        "scaler = preprocessing.StandardScaler()\n",
        "df_trail_3_x_scaled = scaler.fit_transform(df_trail_3_x)"
      ],
      "metadata": {
        "id": "Uq_TpQsAxgCT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = GradientBoostingClassifier(learning_rate = 0.2, n_estimators = 80)"
      ],
      "metadata": {
        "id": "54cmKLVMtBld"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(df_trail_3_x_scaled,df_trail_3_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y08CnljBtBwc",
        "outputId": "d64eff7a-e656-498a-b3f6-eadac2e9d0ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(learning_rate=0.2, n_estimators=80)"
            ]
          },
          "metadata": {},
          "execution_count": 283
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g55TR0yFtCDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##prepare test data\n",
        "1. Prepare tag column\n",
        "2. Drop unnecessary columns\n",
        "3. Change the shape of test_df to match the train_df"
      ],
      "metadata": {
        "id": "aOgayihg89CH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tags_column_test = df_test['tags']"
      ],
      "metadata": {
        "id": "f-Jik0Z19Tfv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tags_df_test = tags_column_test.str.get_dummies(',')\n",
        "tags_df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "WvAHKXHA9adH",
        "outputId": "e5f5c31d-dcd7-43db-f5c2-3372c47173f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>#Sport Bra</th>\n",
              "      <th>#Summer Clothes</th>\n",
              "      <th>#fashion #tshirt</th>\n",
              "      <th>#top #crop</th>\n",
              "      <th>2018fashion</th>\n",
              "      <th>2pieceset</th>\n",
              "      <th>34sleevedres</th>\n",
              "      <th>3D T-shirt</th>\n",
              "      <th>3dprint</th>\n",
              "      <th>3dprintedtshirt</th>\n",
              "      <th>5pointspant</th>\n",
              "      <th>90sfashion</th>\n",
              "      <th>A line dress</th>\n",
              "      <th>Accessories</th>\n",
              "      <th>Active</th>\n",
              "      <th>Adjustable Baseball Cap</th>\n",
              "      <th>Animal</th>\n",
              "      <th>Ankle</th>\n",
              "      <th>Ankle Strap</th>\n",
              "      <th>Anklets</th>\n",
              "      <th>Athletics</th>\n",
              "      <th>Bags</th>\n",
              "      <th>Balloon</th>\n",
              "      <th>Bandage Dresses</th>\n",
              "      <th>Bandages</th>\n",
              "      <th>Baseball</th>\n",
              "      <th>Baseball Cap</th>\n",
              "      <th>Bat</th>\n",
              "      <th>Bath</th>\n",
              "      <th>Bathing</th>\n",
              "      <th>Bathing Suits For Women</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Battery</th>\n",
              "      <th>Beach</th>\n",
              "      <th>Beach Shorts</th>\n",
              "      <th>Beach Shorts Women</th>\n",
              "      <th>Beach slippers</th>\n",
              "      <th>Beading</th>\n",
              "      <th>Beauty</th>\n",
              "      <th>Bikini</th>\n",
              "      <th>...</th>\n",
              "      <th>women top</th>\n",
              "      <th>women trousers</th>\n",
              "      <th>women yoga pants</th>\n",
              "      <th>women's dress</th>\n",
              "      <th>women's jogging suits</th>\n",
              "      <th>women's pajamas</th>\n",
              "      <th>women's pants</th>\n",
              "      <th>women's plus size fashion</th>\n",
              "      <th>women's sleeveless tank</th>\n",
              "      <th>womenbikiniset</th>\n",
              "      <th>womenbohemiandre</th>\n",
              "      <th>womendressshirt</th>\n",
              "      <th>womenhotshirt</th>\n",
              "      <th>womenmonokini</th>\n",
              "      <th>womenonepiece</th>\n",
              "      <th>womens clothes</th>\n",
              "      <th>womens dresses</th>\n",
              "      <th>womens jeans</th>\n",
              "      <th>womens shirt</th>\n",
              "      <th>womens tank tops</th>\n",
              "      <th>womens top</th>\n",
              "      <th>womens vest</th>\n",
              "      <th>womensfahsion</th>\n",
              "      <th>womenshoessummer</th>\n",
              "      <th>womenshortpan</th>\n",
              "      <th>womenshortsleevedre</th>\n",
              "      <th>womensjumpsuitsromper</th>\n",
              "      <th>womenstraplessbra</th>\n",
              "      <th>womensundres</th>\n",
              "      <th>womenswimwearbikini</th>\n",
              "      <th>woven</th>\n",
              "      <th>wrappedchest</th>\n",
              "      <th>yoga leggings</th>\n",
              "      <th>yoga pants</th>\n",
              "      <th>yoga top</th>\n",
              "      <th>yogashort</th>\n",
              "      <th>yogavest</th>\n",
              "      <th>zippers</th>\n",
              "      <th>zippertop</th>\n",
              "      <th>шортымужские</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>479 rows × 1346 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-594a60e8-cf5d-4946-8ad2-7f8e24601eba')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-594a60e8-cf5d-4946-8ad2-7f8e24601eba button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-594a60e8-cf5d-4946-8ad2-7f8e24601eba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     #Sport Bra  #Summer Clothes  ...  zippertop  шортымужские\n",
              "0             0                0  ...          0             0\n",
              "1             0                0  ...          0             0\n",
              "2             0                0  ...          0             0\n",
              "3             0                0  ...          0             0\n",
              "4             0                0  ...          0             0\n",
              "..          ...              ...  ...        ...           ...\n",
              "474           0                0  ...          0             0\n",
              "475           0                0  ...          0             0\n",
              "476           0                0  ...          0             0\n",
              "477           0                0  ...          0             0\n",
              "478           0                0  ...          0             0\n",
              "\n",
              "[479 rows x 1346 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop nominal columns\n",
        "df_test_v1 = df_test.drop(need_encoding_columns,axis = 1)\n",
        "#drop has_urgency_banner \n",
        "df_test_v1 = df_test_v1.drop(to_fill_columns,axis = 1)"
      ],
      "metadata": {
        "id": "XszWgFIeu0oW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test_v1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qd5o42et9II-",
        "outputId": "4a7e2698-b906-4079-d4e1-712c20fbf540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                             0\n",
              "retail_price                      0\n",
              "currency_buyer                    0\n",
              "units_sold                        0\n",
              "uses_ad_boosts                    0\n",
              "rating_count                      0\n",
              "badges_count                      0\n",
              "badge_local_product               0\n",
              "badge_product_quality             0\n",
              "badge_fast_shipping               0\n",
              "product_variation_inventory       0\n",
              "shipping_option_name              0\n",
              "shipping_option_price             0\n",
              "shipping_is_express               0\n",
              "inventory_total                   0\n",
              "urgency_text                    355\n",
              "merchant_title                    0\n",
              "merchant_name                     0\n",
              "merchant_info_subtitle            1\n",
              "merchant_rating_count             0\n",
              "merchant_rating                   0\n",
              "merchant_id                       0\n",
              "merchant_has_profile_picture      0\n",
              "merchant_profile_picture        423\n",
              "theme                             0\n",
              "crawl_month                       0\n",
              "id                                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 287
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = pd.concat([df_test_v1, tags_df_test], axis=1)"
      ],
      "metadata": {
        "id": "lEIKII5e9hEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diff_columns = list(set(df_trail_3_x.columns) - set(x_test.columns))"
      ],
      "metadata": {
        "id": "Zj4ULYXo9sDW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fill all different columns with zeros\n",
        "x_test[diff_columns] = 0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1UeAqyz-Nnf",
        "outputId": "368d59ab-17f2-466c-c5c9-7395b2f5eb20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:3645: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  self[col] = value\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(set(df_trail_3_x.columns) - set(x_test.columns))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IciPy8e5-TxB",
        "outputId": "a4a9118f-e283-49d3-c5b3-edecdb2dc6a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 295
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#rearrange column order\n",
        "x_test = x_test[df_trail_3_x.columns]"
      ],
      "metadata": {
        "id": "2wfArE-E_REv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#match the type for each column\n",
        "\n",
        "x_test = x_test.astype(df_trail_3_x.dtypes)"
      ],
      "metadata": {
        "id": "e-N-oY5iGxMc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "amIA47Eq_dyO",
        "outputId": "d8bb7cad-a694-48d3-f7a0-1b3fb93b1808"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>merchant_rating</th>\n",
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              "      <th>#fashion #shorts</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>16.00</td>\n",
              "      <td>14</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3.367133</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>6.00</td>\n",
              "      <td>17</td>\n",
              "      <td>5000</td>\n",
              "      <td>1</td>\n",
              "      <td>817</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4.079644</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>4.83</td>\n",
              "      <td>51</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.824561</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>3.00</td>\n",
              "      <td>3</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.056169</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>13.00</td>\n",
              "      <td>11</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4.032956</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>479 rows × 2147 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d55d0dad-d543-4dba-a244-22d5042ad038')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d55d0dad-d543-4dba-a244-22d5042ad038 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d55d0dad-d543-4dba-a244-22d5042ad038');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     price  retail_price  units_sold  ...  zippers  zippersleevelessvest  漁師の帽子\n",
              "0     5.75             5          10  ...        0                     0      0\n",
              "1     7.00             7        5000  ...        0                     0      0\n",
              "2     5.00            16        1000  ...        0                     0      0\n",
              "3    18.00            27        5000  ...        0                     0      0\n",
              "4    12.00            81       20000  ...        0                     0      0\n",
              "..     ...           ...         ...  ...      ...                   ...    ...\n",
              "474  16.00            14         100  ...        0                     0      0\n",
              "475   6.00            17        5000  ...        0                     0      0\n",
              "476   4.83            51          50  ...        0                     0      0\n",
              "477   3.00             3         100  ...        0                     0      0\n",
              "478  13.00            11         100  ...        0                     0      0\n",
              "\n",
              "[479 rows x 2147 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_StandardScaler = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "0O562ttSBSGV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##predict"
      ],
      "metadata": {
        "id": "OyL-OQYb_jL2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred_test = model.predict(x_test_StandardScaler)"
      ],
      "metadata": {
        "id": "_DXY1mlyANi3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxra1xnqAo_f",
        "outputId": "be10f31b-0f94-480f-a3b6-fcfa62ad92e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       937\n",
              "1       270\n",
              "2       308\n",
              "3       913\n",
              "4       403\n",
              "       ... \n",
              "474    1166\n",
              "475    1416\n",
              "476      63\n",
              "477     626\n",
              "478     196\n",
              "Name: id, Length: 479, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_df = pd.DataFrame(data={'id': df_test.id, 'rating': pred_test})"
      ],
      "metadata": {
        "id": "CL7x7K0lAWp1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_df.rating.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WWB-Y9SAn3e",
        "outputId": "c2061e4e-7356-4496-eada-5d78c6756493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    399\n",
              "3.0     51\n",
              "5.0     28\n",
              "2.0      1\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmmkvUPqIMLd",
        "outputId": "431c9d36-c9bc-42ce-e17b-263970a84e8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 479 entries, 0 to 478\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   id      479 non-null    int64  \n",
            " 1   rating  479 non-null    float64\n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 7.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_df.to_csv('pred_walkthrough.csv', index=False)"
      ],
      "metadata": {
        "id": "UAoUIq22A1Q9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5L2duaNTAwYA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Answers to Questions"
      ],
      "metadata": {
        "id": "MQO7NbiNSBXP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Why Data Mining is a misnomer? What is another preferred name?\n",
        "\n",
        "* Because when someone hears the phrase \"Data mining\" he thinks that we mine for data like collecting data from many sources.\n",
        "But what we do is mining the data, we extract knowledge from the data.\n",
        "\n",
        "* Another preferred name is Mining Data\n",
        "Or Mining the data or knowledge extraction"
      ],
      "metadata": {
        "id": "Rpn86K0mSI5V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. What is the general knowledge discovery process? What is the difference between a data engineer and data scientist/AI engineer?"
      ],
      "metadata": {
        "id": "nJrDQUsrT_o3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. In data mining, what is the difference between prediction and categorization?"
      ],
      "metadata": {
        "id": "ulj7MVM8UBzr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Why data science/machine learning is a bad idea in the context of information security?\n",
        "* Because models carries the informations of the training data for example using model weights we can reproduce the training set used for training the model. or we can know if a certain sample (image) is used for training the model by feeding the image to the model and observer the confidence. And we can find out if certain class wasn't included in the training set."
      ],
      "metadata": {
        "id": "XjHFDgbRUCgn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. What is CIA principle and how can we use it to access the security/privacy aspect of the AI system/pipelines?\n",
        "* CIA standa for Confidentiality,Integrity and Availability. We want to preserve these three components for every project."
      ],
      "metadata": {
        "id": "K8qrbUskUCqe"
      }
    }
  ]
}